Built with R version:
3.5.0


Built with R version:
3.5.0

Libraries

Load necessary libraries

library(affy)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply,
    parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colMeans, colnames, colSums, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans,
    rownames, rowSums, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.
library(ComplexHeatmap)
Loading required package: grid
========================================
ComplexHeatmap version 1.18.1
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://bioconductor.org/packages/ComplexHeatmap/

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================
library(plot3D)
library(gplots)

Attaching package: ‘gplots’

The following object is masked from ‘package:stats’:

    lowess
library(circlize)
========================================
circlize version 0.4.4
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: http://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization 
  in R. Bioinformatics 2014.
========================================
library(AnnotationDbi)
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

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    space

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    expand.grid
library(limma)

Attaching package: ‘limma’

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    plotMA
library(lattice)
library(org.Hs.eg.db)
library(MASS)

Attaching package: ‘MASS’

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    select
library(RColorBrewer)
library(AnnotationDbi)
library(rglwidget)
The functions in the rglwidget package have been moved to rgl.
###library(hgu133plus2hsentrezgcdf)
library(VennDiagram)
Loading required package: futile.logger
library(org.Hs.eg.db)
library(GenomicRanges)
Loading required package: GenomeInfoDb
library(GenomicFeatures)
library(rtracklayer)
library(biomaRt)
library(glmnet)
Loading required package: Matrix

Attaching package: ‘Matrix’

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    expand

Loading required package: foreach
Loaded glmnet 2.0-16
library(survival)
library(Hmisc)
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Loading required package: ggplot2

Attaching package: ‘Hmisc’

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    contents

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    contents

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    format.pval, units
library(ConsensusClusterPlus)
library(pheatmap)
library(ggplot2)
library(heatmap.plus)
library(rgl)
library(caret)

Attaching package: ‘caret’

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    cluster
library(e1071)

Attaching package: ‘e1071’

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    impute
library(tmod)
set1 = c(brewer.pal(9,"Set1"), brewer.pal(8, "Dark2"))
violinJitter <- function(x, magnitude=1){
  d <- density(x)
  data.frame(x=x, y=runif(length(x),-magnitude/2, magnitude/2) * approxfun(d$x, d$y)(x))
}
rotatedLabel <- function(x0 = seq_along(labels), y0 = rep(par("usr")[3], length(labels)), labels, pos = 1, cex=1, srt=45, ...) {
  w <- strwidth(labels, units="user", cex=cex)
  h <- strheight(labels, units="user",cex=cex)
  u <- par('usr')
  p <- par('plt')
  f <- par("fin")
  xpd <- par("xpd")
  par(xpd=NA)
  text(x=x0 + ifelse(pos==1, -1,1) * w/2*cos(srt/360*2*base::pi), y = y0 + ifelse(pos==1, -1,1) * w/2 *sin(srt/360*2*base::pi) * (u[4]-u[3])/(u[2]-u[1]) / (p[4]-p[3]) * (p[2]-p[1])* f[1]/f[2] , labels, las=2, cex=cex, pos=pos, adj=1, srt=srt,...)
  par(xpd=xpd)
}
avefc = function (y, log=TRUE, replace= FALSE) {
     if (log) y = 2^y
   if (replace) y = y + (1-min(y))
   m = apply(y,1,mean)
     y.n = y/m  
     y.n2 = y.n
     y.n2 [y.n2 < 1] = 1/ (y.n2 [y.n2 < 1])
     ave.fc = apply (y.n2, 1, mean)
     return(ave.fc)
     }

Ensembl Library

For gene convertion from array to HUGO

ensembl = useMart( "ensembl", dataset = "hsapiens_gene_ensembl" )

Gene Expression Data

Upload or generate GEP normalized matrix

### choice 1: import processed matrix
# data.dir="./Rmd.files/"
data.dir = '/Users/emagene/Dropbox/codes/github/PTCL/'
setwd(data.dir)
load (file.path(data.dir,"/Rmd.files/541_PTCL_batch_adjusted_geo.id.Rdata"))
geneExpr = adj.data
# import batch and re-order accordingly
load(file.path(data.dir,"/Rmd.files/PTCL.batch.Rdata"))
batch = batch [order(batch$nameNEW),]
batch.series = as.vector(batch$center)
batch$cancer = "cancer"
# ### OPTIONAL: CHECK BATCH ON FINAL.MOLEC
# 
# #mod = model.matrix(~as.factor(center), data=batch)
# mod = model.matrix(~as.factor(final.molec), data=design)
# mod0 = model.matrix(~1, data= batch)
# library(sva)
# n.sv = num.sv(adj.data,mod,method="leek")
# svobj = sva(adj.data,mod,mod0,n.sv=n.sv)
# 
# pValues = f.pvalue(adj.data,mod,mod0)
# qValues = p.adjust(pValues,method="BH")
# modSv = cbind(mod,svobj$sv)
# mod0Sv = cbind(mod0,svobj$sv)
# pValuesSv = f.pvalue(adj.data,modSv,mod0Sv)
# qValuesSv = p.adjust(pValuesSv,method="BH")
### end of choice 1
### choice 2: generate your own affy object and custom data
# download CEL files from GEO series GSE6338, GSE19067, GSE19069, GSE40160, GSE58445, GSE65823 and EBI series ETABM702, ETABM783
# GSM368580.CEL, GSM368582.CEL, GSM368584.CEL, GSM368586.CEL, GSM368589.CEL, GSM368591.CEL, GSM368594.CEL, GSM472164.CEL, GSM1411278.CEL, GSM1411284.CEL, GSM1411285.CEL, GSM1411287.CEL, GSM1411355.CEL, GSM1411364.CEL, GSM1411368.CEL, GSM1411425.CEL, GSM1411427.CEL excluded from the analysis (see Methods for explaination")
### celfiles <- dir("~/Documents/DATI/PTCL.nos/GSE6338-GSE19067-GSE19069-GSE40160-GSE58445-GSE65823-ETABM702-ETABM783/", pattern = ".CEL")
### library(affy)
### gset = justRMA(celfile.path = "/Users/emagene/Documents/DATI/PTCL.nos/GSE6338-GSE19067-GSE19069-GSE40160-GSE58445-GSE65823-ETABM702-ETABM783/", ### filenames = celfiles, sampleNames = gsub(".CEL","", celfiles), cdfname = "hgu133plus2hsentrezgcdf")
### geneExpr = exprs(gset)
### batch adjustment
### library(sva)  
### # import batch and re-order accordingly
### load("./Rmd.files/PTCL.batch.Rdata")
### batch = batch [order(rownames(batch)),]
### batch.series = as.vector(batch$center)
### geneExprNEW = geneExpr [ , order(colnames(geneExpr)) ]
### geneExprNEW = geneExprNEW[grep("AFFX",rownames(geneExprNEW), invert=TRUE),]
### # check order correspondence and, if correct, adjust data
### if (all(colnames(geneExprNEW) == rownames(batch))) {
###   adj.data = ComBat (geneExprNEW, batch.series, mod = NULL, par.prior = TRUE, prior.plots = TRUE)
### } else {
###   cat("Error: colnames and batch did not correspond")
### }
### geneExpr = adj.data
### colnames(geneExpr) = as.vector(batch$nameNEW)
### end of choice 2

Clinical Data

Upload paz info with clinical and mutational data

pts.info.data <- read.table("./Rmd.files/541_paz_info_MUT.txt", sep="\t", header=TRUE, check.names=FALSE, stringsAsFactors = F)
# customize colors for categories
levels(as.factor(pts.info.data$final.molec))
 [1] "AITL"     "ALCL.neg" "ALCL.pos" "ATLL"     "NKT"      "PTCL.nos" "T.CD30"   "T.CD4"    "T.CD8"    "T.DR"     "T.reg"    "TCR-HL"  
# "AITL"     "ALCL.neg" "ALCL.pos" "ATLL"     "NKT"      "PTCL.nos" "T.CD30"   "T.CD4"    "T.CD8"    "T.DR"     "T.reg"    "TCR-HL"  
colorz = c("black", "yellow","dodgerblue2","brown2","darkorchid1", "orange", "grey42", "grey52","grey62","grey72","grey82","grey92")
temp = split (  pts.info.data$sample.nameNEW, pts.info.data$final.molec )
colorx = colnames(geneExpr)
length(colorz)
[1] 12
length(temp)
[1] 12
for (i in 1:length(colorz)) colorx [ which(colorx %in% unlist(temp[i])) ] = colorz[i]
library(gplots)
colorx = col2hex(colorx)
### build design matrix and transform to numerical 
design <- pts.info.data[,c(1:2,6:8,14:17)]
rownames(design)<- design[,1]
design<- design[,-c(1:2)]
#design<-na.omit(design) ### select onyl patients with all mutations data available (n=53)
design$age<- as.numeric(as.character(design$age))
design$age<- design$age - median(design$age)
design[design == "WT"] <- 0
design[design == "MUT"] <- 1
design$final.molec[design$final.molec=="AITL"] <- 0
design$final.molec[design$final.molec=="PTCL.nos"] <- 1
design$final.molec[design$final.molec=="ALCL.neg"] <- 2
design$final.molec[design$final.molec=="ALCL.pos"] <- 3
design$final.molec[design$final.molec=="ATLL"] <- 4
design$final.molec[design$final.molec=="NKT"] <- 5
design$final.molec[477:541] <- 6
design$gender[design$gender=="M"] <- 1
design$gender[design$gender=="F"] <- 0
design$age = NULL
all(pts.info.data$sample.nameNEW == batch$nameNEW) 
[1] TRUE

Pie Chart with Percentages

slices <- table(pts.info.data$final.molec)
lbls <- names(table(pts.info.data$final.molec))
pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, ": ", slices, " (", pct, "%)", sep="" ) # add percents to labels
#pdf("Figure_1a_pie_plot.pdf", width = 5, height = 5)
par(mfrow=c(1,1))
par(mar=c(3,3,3,3), xpd=F)
pie(slices,labels = lbls, init.angle = 0, col=colorz, main="", cex=0.6, radius=0.8)

#dev.off()

PCA

# apply variational filter
afc2 = avefc(geneExpr, log=TRUE, replace=FALSE)
data541exprs.vf = geneExpr [afc2 >= 2, ]
dim(data541exprs.vf )
[1] 1840  541
# retry PCA on shorted gene list
data541m = t(as.matrix(data541exprs.vf))
pca<-prcomp(data541m,scale=T)
mfrow3d(nr = 1, nc = 1, sharedMouse = T)  
plot3d(pca$x,rgl.use=F,col=colorx,size=0.6,type="s")
rglwidget()

Heatmap

mat = as.matrix(data541exprs.vf)
base_mean = rowMeans(mat)
mat_scaled = t(apply(mat, 1, scale))
types = pts.info.data$final.molec
color.annot = col2hex(colorz); names (color.annot)= names(temp)  
ha = HeatmapAnnotation(df = data.frame(type = types) , col = list(type = c( color.annot ) ) )
ha@anno_list[[1]]@color_mapping@colors = col2hex(colorz)
names(ha@anno_list[[1]]@color_mapping@colors) = names(temp)
ht = Heatmap(mat_scaled, name = "expression", km = 7, clustering_method_columns = "ward.D", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), top_annotation = ha, top_annotation_height = unit(4, "mm"), show_row_names = FALSE, show_column_names = FALSE)
column_order(ht)
  [1]  91 236  85  86 429  88  64 311 270 271 229 231 180 257  87 227 258 304 309 436 431 234 148 147  31  29 450 281 500 521 499 273 272 269 522 520 501 519 498 518 497 505 506 504 486
 [46] 485 482 483 484 502 503 524 527 529 528 530 532 531 525 526 523 434  61 182 176  63  83  66 445 230 515 517 513 296 298 516 494 492 496 493 314 297 294 295 293 289 292 512 507 511
 [91] 510 509 291 290 514 508 448 447 444 440 396 397 395 394 391  50  51 187 192 433 349 363 385 428 435 427 389 366 491 401 334 459 392 402 388 457 463 446 380 351 474 372 376 489 487
[136] 537 488 534 536 495 490 535 533 481 479 478 480 477 264 261 268 259 262 253 267 265 266 263  65 254 256 255 318 308 320 319 315 324 326 313 280 299 285 316 286 278 344 330 430 331
[181] 275 305 307 136 426 184 162 167 422 421 469 341 245 160 179 153 443 449 181 149 368 186 164 141 194 218 161  98 300 312  84 442 323 301 174 306 248 329 325 178 139 145 142 144 185
[226] 157 249 172 177 158 183 150 170 171 152 131 134 137 216 214 213 241 202 205 203 191 130 133 195 215 244 211 246 238 197 224 226 225 169 538 168 539 540 219 223 220 221 222 206 204
[271] 232 242 200 243 143 250 154 207 198 235 240 247 252 163 155 383 188 239 417 276 406  93 303 339 109  53 124  82  26  32  23  22  20 189  18 398  41  44  10 193 217 129   2 140 208
[316] 251 199 201 209 210 212 233 237 288 284 420 439 274 352 287 441 328 283 282 321 317 310 279 322 332 302 348 419 399 387 166 159 337 467 151 277 355 359 410 353 470 475 466 175 370
[361]  42 411 374 393 404 327 456  94 432 541 156 173 146 165 135 128 106 458 361 364 354  90 454  27 403 138 196 462 453 338 415 407 379 371 464 461 110  79 121  45 405 425  96 102 424
[406] 423 360 452 451 365 358  89   1  28  19 347 101 260 455  59 418 367  48  69 190 333 132  17 378 350 468 346   9 414 409 413 412 408 116  70 122 123 340 381 460 382  77  38 108  37
[451] 228  49  68  67 416  62  92  71   4 103 126  36 120 127  56  74  13  12  72 104 105 100  21  30  25  33  24 465 345 117 343 342 471 336 476 356  15  60 390 357 362  99  97 437  39
[496]  78 438  80 375  40 113 112  52  58 400  57  55  54  76  11 114  73  75 472   3   6 377 119  47  35 125   5  95  81  34  46  43  16  14 386   8 473 335 111 384   7 118 373 369 107
[541] 115

Check relative log expression after batch correction

rle.custom = function (a, logged2 = TRUE, file = NULL, colorbox= NULL, labels=NULL , legend = NULL ) {
    a.m <- apply(a,1,median)
if (logged2) {
    for (i in 1:dim(a)[2]) {
         a [,i] <-  a [,i] - a.m
    }
    } else {
        for (i in 1:dim(a)[2]) {
         a [,i] <-  log (a [,i] / a.m )
    }
    }
   # png(file,10240,3840)
    par(mar=c(10,4,6,2))
    boxplot (a, ylim= c(-5,5), outline=F, col=colorbox, xlab="pts", names=labels, las=2, cex.axis = 1.5, main="RLE", xlim = c(1,600), cex.main = 5 )
    legend("bottomright",legend = c(levels(as.factor(pts.info.data$final.molec))),   
      fill = colorz, # 6:1 reorders so legend order matches graph
      title = "Legend",
      cex = 5)
  #  dev.off()
    a.c = apply(a, 2, stats::quantile)
    return(a.c)
}
#rle.medians = rle.custom(geneExpr, colorbox=colorx, file="./RLE.541.png", labels=pts.info.data$sample.nameNEW )
#plot(rle.medians[3,], type="l", xlab="pts", ylab="RLE median" )
rle.medians = rle.custom(geneExpr, colorbox=colorx, file="./RLE.541.png", labels=pts.info.data$sample.nameNEW )

plot(rle.medians[3,], type="l", xlab="pts", ylab="RLE median" )

Final Gene Expression Matrix

Define design file and filter geneExpr for patients included in design data frame and

design <- pts.info.data[,c(1:2,6:8,14:17)]
rownames(design)<- design[,1]
design<- design[,-c(1:2)]
design<-na.omit(design) ###  select onyl patients with all mutations data available (n=53)
design$age<- as.numeric(as.character(design$age))
design$age<- design$age - median(design$age)
design[design == "WT"] <- 0
design[design == "MUT"] <- 1
design$final.molec[design$final.molec=="AITL"] <- 0
design$final.molec[design$final.molec=="PTCL.nos"] <- 1
design$gender[design$gender=="M"] <- 1
design$gender[design$gender=="F"] <- 0
design$offset <- rep(1, nrow(design))
design<-design[,c(8,1:7)]
all(pts.info.data$sample.nameNEW == colnames(geneExpr)) ## check correspondence
[1] TRUE
# geneExpr = geneExpr [ , order (pts.info.data$geo.id)] ### do only to set correspondence in case of custom procedure
# colnames(geneExpr) = pts.info.data$sample.nameNEW [ order (pts.info.data$geo.id)]
geneExpr2<- (geneExpr[, rownames(design)])
geneExpr2<- data.matrix(geneExpr2, rownames.force = NA)
design<- data.matrix(design, rownames.force = NA)

Model fitting

We use the lmFit function from the limma package. This comes with a whole series of powerful and reliable tests.

glm = lmFit(geneExpr2[,rownames(design)], design = design )
glm = eBayes(glm)
F.stat <- classifyTestsF(glm[,-1],fstat.only=TRUE)
glm$F <- as.vector(F.stat)
df1 <- attr(F.stat,"df1")
df2 <- attr(F.stat,"df2")
if(df2[1] > 1e6){
  glm$F.p.value <- pchisq(df1*glm$F,df1,lower.tail=FALSE)
}else
  glm$F.p.value <- pf(glm$F,df1,df2,lower.tail=FALSE)
set.seed(12345678)
rlm <- lmFit(geneExpr[,rownames(design)], apply(design, 2, sample))
rlm <- eBayes(rlm)
F.stat <- classifyTestsF(rlm[,-1],fstat.only=TRUE)
rlm$F <- as.vector(F.stat)
df1 <- attr(F.stat,"df1")
df2 <- attr(F.stat,"df2")
if(df2[1] > 1e6){
  rlm$F.p.value <- pchisq(df1*rlm$F,df1,lower.tail=FALSE)
}else
  rlm$F.p.value <- pf(rlm$F,df1,df2,lower.tail=FALSE)
F.stat <- classifyTestsF(glm[,2:5],fstat.only=TRUE)
df1 <- attr(F.stat,"df1")
df2 <- attr(F.stat,"df2")
F.p.value <- pchisq(df1*F.stat,df1,lower.tail=FALSE)
R.stat <- classifyTestsF(rlm[,2:5],fstat.only=TRUE)
Rall = 1 - 1/(1 + glm$F * (ncol(design)-1)/(nrow(design)-ncol(design)))
Rgenetics = 1 - 1/(1 + F.stat * 4/(nrow(design)-ncol(design)))
Pgenetics = 1 - 1/(1 + R.stat * 4/(nrow(design)-ncol(design)))
names(Rgenetics) <- names(Pgenetics) <- names(Rall) <-  rownames(geneExpr)

Differentially Expressed Genes

par(bty="n", mgp = c(2,.33,0), mar=c(3,2.5,1,1)+.1, las=1, tcl=-.25, xpd=NA)
d <- density(Pgenetics,bw=1e-3)
f <- 40 #nrow(gexpr)/512
#pdf("Figure_2a_MAY.pdf", width = 10, height = 7)
par(mfrow=c(1,1))
par(mar=c(8,5,5,5), xpd=F)
plot(d$x, d$y * f, col='grey', xlab=expression(paste("Explained variance per gene ", R^2)), main="", lwd=2, type="l", ylab="", xlim=c(0,1), cex.axis=1.2, cex.lab=1.5, bty="n")
title(ylab="Density", line=2.5, cex.lab=1.5)
d <- density(Rgenetics, bw=1e-3)
r <- min(Rgenetics[p.adjust(F.p.value,"BH")<0.01]) ######## threshold to select 412 genes
x0 <- which(d$x>r)
polygon(d$x[c(x0[1],x0)], c(0,d$y[x0])* f, col=paste(set1[1],"44",sep=""), border=NA)
lines(d$x, d$y* f, col=set1[1], lwd=2)
text(d$x[x0[1]], d$y[x0[1]]*f +20, pos=4, paste(sum(Rgenetics > r), "genes q < 0.01"))
legend("topright", bty="n", col=c(set1[1], "grey"), lty=1, c("Observed","Random"), lwd=2)

#dev.off()
glmPrediction <- glm$coefficients %*% t(design)
rlmPrediction <- rlm$coefficients %*% t(design)

Print signficiant genes

kk<-as.data.frame((p.adjust(F.p.value,"BH")<0.01))
kk$gene<- rownames(kk)
colnames(kk)[1]<-"code"
kk2<-kk[kk$code=="TRUE",]
### sort(kk2$gene) ##### if you want to print the entire list of differentially expressed genes

Significant effects per covariate

Extract the list of differentially expressed genes by mutations

### customize colors in colMutations
# colMutations = c(brewer.pal(8,"Set1")[-6], rev(brewer.pal(8,"Dark2")), brewer.pal(7,"Set2"))[c(1:12,16:19,13:15)]
# o <- order(apply(col2rgb(colMutations),2,rgb2hsv)[1,])
# colMutations <- colMutations[rev(o)][(4*1:19 +15) %% 19 + 1][1:7]
colMutations = col2hex(c("magenta", "purple","gray60","red","lightblue","green","orange"))
names(colMutations) <- colnames(design)[-1]
gene_code<- kk2$gene
tab=NULL
for(i in (1:length(kk2$gene)))
{
  gene_single<- gene_code[i]
  y <- glm$coefficients[gene_single,-1]+glm$coefficients[gene_single,1]
  w <- glm$p.value[gene_single,-1] < 0.05
  int<-c(gene_single, as.character(w))
  tab<- rbind(tab, int)
}
rownames(tab)<-seq(1:nrow(tab))
colnames(tab)<- c("gene",colnames(design)[-1])
# Write to disk a file with all significant genes
#write.table(tab, "table_differentially_expressed_gene.txt",sep="\t", quote=F, row.names = F, col.names = T)

Example of extraction

 # temp_name = unique(getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",gene_single),
 # mart = ensembl)$external_gene_name)
  #pdf("Figure_2b.pdf", width = 10, height = 7)
  par(mfrow=c(1,1))
  par(mar=c(10,8,5,5), xpd=F)
  par(bty="n", mgp = c(1.5,.33,0),las=1, tcl=-.25, xpd=F)
  temp_name<- "YAP1"
  plot(glmPrediction[gene_single,], geneExpr[gene_single,rownames(design)], ylab="", xlab="",
       pch=16, cex=1, cex.axis=1.2, cex.lab=1.5)
  title(ylab=(paste("Observed ",temp_name, " expression")), line=2.5, cex.lab=1.5)
   title( xlab=(paste("Predicted ",temp_name, " expression")), line=2.5, cex.lab=1.5)
  abline(0,1)
  u <- par("usr")
  par(xpd=NA)
  y <- glm$coefficients[gene_single,-1]+glm$coefficients[gene_single,1]
  u <- par("usr")
  x0 <- rep(u[3]+1,ncol(design)-1)
  y0 <- u[4] + 0.05*(u[4]-u[3]) - rank(-y)/length(y) * (u[4]-u[3])/1.2
  d <- density(y)
  lines(d$x, d$y/5+1+u[3], col="grey")
  lines(d$x, -d$y/5+1+u[3], col="grey")
points(x=y, y=x0+violinJitter(y, magnitude=0.25)$y, col=colMutations, pch=16, cex=1.5)
  text(x=glm$coefficients[gene_single,1], y= 5.2, "Model coefficients", cex=0.8)
legend("topleft",names(colMutations), col = colMutations, bty= "n", cex = 1.2, pch = 16)

#dev.off()

Plot significant effects per covariate (q<0.01)

testResults <- decideTests(glm, method="hierarchical",adjust.method="BH", p.value=0.01)[,-1]
significantGenes <- sapply(1:ncol(testResults), function(j){
  c <- glm$coefficients[testResults[,j]!=0,j+1]
  table(cut(c, breaks=c(-5,seq(-1.5,1.5,l=7),5)))
})
colnames(significantGenes) <- colnames(testResults)
rownames(tab)<-c(1:nrow(tab))
tab2<- as.data.frame(tab)
tab2$gene<-as.character(as.character(tab2$gene))
tab2$final.molec<-as.character(as.character(tab2$final.molec))
tab2$TET2<-as.character(as.character(tab2$TET2))
tab2$RHOA<-as.character(as.character(tab2$RHOA))
tab2$IDH2<-as.character(as.character(tab2$IDH2))
tab2$DNMT3A<-as.character(as.character(tab2$DNMT3A))
#  pdf("Figure_2c.pdf", width = 10, height = 7)
  par(mfrow=c(1,1))
  par(mar=c(8,8,5,5), xpd=F)
par(mfrow=c(1,1))
par(bty="n", mgp = c(2.5,.33,0), mar=c(5,5.5,5,0)+.1, las=2, tcl=-.25)
b <- barplot(significantGenes, las=2, ylab = "Differentially expressed genes", col=brewer.pal(8,"RdYlBu"), legend.text=FALSE , border=0, xaxt="n", cex.lab=1.5)#, col = set1[simple.annot[names(n)]], border=NA)
rotatedLabel(x0=b-0.1, y0=rep(-0.5, ncol(significantGenes)), labels=colnames(significantGenes), cex=1.2, srt=45, font=ifelse(grepl("[[:lower:]]", colnames(design))[-1], 1,3), col=colMutations)
rotatedLabel(b-0.1, colSums(significantGenes), colSums(significantGenes), pos=3, cex=, srt=45)#dev.off()
clip(0,30,0,1000)
x0 <- 7.5
image(x=x0+c(0,0.8), y=par("usr")[4]+seq(-1,1,l=9) -4, z=matrix(1:8, ncol=8), col=brewer.pal(8,"RdYlBu"), add=TRUE)
text(x=x0+1.1, y=par("usr")[4]+c(-1,0,1) -4, format(seq(-1,1,l=3),2), cex=0.66)
lines(x=rep(x0+0.9, 2), y=par("usr")[4]+c(-1,1) -4)
segments(x0+0.9,par("usr")[4] + 1-4,x0+0.95,par("usr")[4] + 1-4)
segments(x0+0.9,par("usr")[4] + 0-4,x0+0.95,par("usr")[4] + 0-4)
segments(x0+0.9,par("usr")[4] + -1-4,x0+0.95,par("usr")[4] + -1-4)
text(x0 + 0.45, par("usr")[4] + 1.5-4, "log2 FC", cex=.66)

#dev.off()
# par(bty="n", mgp = c(2.5,.33,0), mar=c(3,3.3,3,0)+.1, las=1, tcl=-.25)
# t <- table(rowSums(abs(testResults[,1:6])))
# b <- barplot(t[-1],ylab="Differentially expressed genes", col=rev(brewer.pal(7, "Spectral")[-(4:5)]), border=NA)
# rotatedLabel(b-0.1, t[-1], t[-1], pos=3, cex=1, srt=45)
# title(xlab="Associated drivers", line=2)

Print the list of differently expressed genes using the Ensembl annotation

select_hist<- pts.info.data[pts.info.data$final.molec == "AITL" |  pts.info.data$final.molec == "PTCL.nos",]
gene<- as.data.frame(testResults)
sig_genes<- gene[gene$final.molec!= 0 |gene$IDH2 != 0 | gene$TET2 != 0 | gene$DNMT3A != 0 | gene$RHOA != 0,]
list_genes<-sort(rownames(sig_genes)) ##### list of signficiant genes
geneannotation1 <- getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",list_genes), mart = ensembl)
sort(unique(geneannotation1$external_gene_name))
 [1] "ADRA2A"     "AL441992.1" "ARHGEF10"   "C3"         "COL4A4"     "DZIP1"      "EFNB2"      "HS3ST3A1"   "ID2"        "NETO2"      "OSMR"       "PRRX1"      "ROBO1"     
[14] "SLC5A3"     "XKR4"       "YAP1"      

Generate a heatmap with AITL, PTCL-NOS with the extracted differentially expressed genes.

gep<- geneExpr[,select_hist$sample.nameNEW]
mat<- gep[list_genes,]
setdiff(rownames(mat), paste0(unique(geneannotation1$entrezgene),"_at"))
character(0)
for (ii in 1:nrow(mat)) {
  #if(length (which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])) != 0 ) rownames(mat) [ii] = geneannotation1$external_gene_name [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
  rownames(mat) [ii] = unique(geneannotation1$external_gene_name) [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
}
mycol= c("red","white","yellow")
mylabel = select_hist[,c("sample.nameNEW","final.molec","IDH2","RHOA","TET2","DNMT3A")]
rownames(mylabel) = mylabel$sample.nameNEW
mylabel$sample.nameNEW = NULL
mylabel.nocol = mylabel
mylabel.col = mylabel
mylabel.col[is.na(mylabel.col)]<-0
#head(mylabel.col)
mylabel.col$final.molec[mylabel.col$final.molec == "AITL"] = "black"; mylabel.col$final.molec[mylabel.col$final.molec == "PTCL.nos"] = "orange"
for (a in 2:5) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol )
mat  <- mat - rowMeans(mat)
par(mfrow=c(1,1))
cluster.pts.nr = pheatmap(mat, annotation_col = mylabel.nocol, annotation_colors = list(final.molec = c(AITL = "black", PTCL.nos = "orange"), filename= "x.pdf",
                                  IDH2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  RHOA = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  TET2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  DNMT3A = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]) ) , show_colnames = F, cellheight = 15, 
         border_color= NA, color = colorRampPalette(rev(brewer.pal(n = 5 , name = "RdYlBu")))(20), scale = "row", clustering_method = "ward.D2",clustering_distance_cols = "euclidean" , silent = F)

### export pts order
cluster.pts.nr$tree_col$labels [cluster.pts.nr$tree_col$order]
  [1] "PTCL.nos..23"  "PTCL.nos..428" "PTCL.nos..448" "PTCL.nos..124" "PTCL.nos..247" "PTCL.nos..463" "PTCL.nos..89"  "PTCL.nos..156" "PTCL.nos..432" "PTCL.nos..216" "PTCL.nos..25" 
 [12] "PTCL.nos..87"  "PTCL.nos..94"  "PTCL.nos..98"  "PTCL.nos..105" "PTCL.nos..93"  "PTCL.nos..195" "AITL..413"     "PTCL.nos..531" "PTCL.nos..143" "PTCL.nos..46"  "PTCL.nos..28" 
 [23] "PTCL.nos..185" "PTCL.nos..416" "PTCL.nos..112" "PTCL.nos..424" "PTCL.nos..134" "PTCL.nos..32"  "PTCL.nos..22"  "PTCL.nos..194" "PTCL.nos..30"  "PTCL.nos..211" "PTCL.nos..52" 
 [34] "PTCL.nos..97"  "PTCL.nos..201" "PTCL.nos..27"  "PTCL.nos..68"  "PTCL.nos..139" "PTCL.nos..72"  "PTCL.nos..120" "PTCL.nos..444" "PTCL.nos..24"  "PTCL.nos..15"  "PTCL.nos..109"
 [45] "PTCL.nos..29"  "PTCL.nos..100" "PTCL.nos..171" "PTCL.nos..104" "PTCL.nos..99"  "PTCL.nos..126" "PTCL.nos..258" "AITL..536"     "PTCL.nos..535" "PTCL.nos..20"  "PTCL.nos..102"
 [56] "PTCL.nos..452" "PTCL.nos..529" "PTCL.nos..90"  "PTCL.nos..230" "PTCL.nos..231" "PTCL.nos..232" "PTCL.nos..236" "PTCL.nos..16"  "PTCL.nos..189" "PTCL.nos..506" "PTCL.nos..519"
 [67] "PTCL.nos..151" "PTCL.nos..186" "AITL..419"     "PTCL.nos..455" "PTCL.nos..47"  "PTCL.nos..213" "PTCL.nos..161" "PTCL.nos..61"  "PTCL.nos..209" "PTCL.nos..119" "PTCL.nos..80" 
 [78] "PTCL.nos..34"  "PTCL.nos..440" "AITL..19"      "AITL..18"      "PTCL.nos..504" "PTCL.nos..118" "PTCL.nos..293" "PTCL.nos..92"  "PTCL.nos..251" "PTCL.nos..101" "PTCL.nos..446"
 [89] "AITL..479"     "PTCL.nos..469" "AITL..411"     "AITL..473"     "AITL..472"     "AITL..481"     "AITL..487"     "PTCL.nos..434" "PTCL.nos..180" "PTCL.nos..135" "PTCL.nos..445"
[100] "PTCL.nos..408" "PTCL.nos..409" "PTCL.nos..460" "PTCL.nos..468" "PTCL.nos..470" "PTCL.nos..471" "PTCL.nos..441" "PTCL.nos..451" "AITL..12"      "PTCL.nos..237" "AITL..165"    
[111] "PTCL.nos..178" "AITL..458"     "AITL..191"     "AITL..163"     "AITL..187"     "AITL..62"      "PTCL.nos..249" "AITL..250"     "AITL..257"     "AITL..110"     "AITL..260"    
[122] "AITL..60"      "AITL..77"      "AITL..84"      "AITL..106"     "AITL..74"      "AITL..133"     "AITL..113"     "AITL..127"     "AITL..44"      "AITL..82"      "AITL..197"    
[133] "AITL..223"     "AITL..17"      "AITL..523"     "AITL..530"     "AITL..154"     "AITL..45"      "AITL..505"     "AITL..2"       "AITL..238"     "AITL..11"      "AITL..259"    
[144] "AITL..10"      "AITL..234"     "AITL..435"     "PTCL.nos..239" "AITL..6"       "AITL..438"     "AITL..518"     "AITL..532"     "AITL..256"     "AITL..449"     "AITL..129"    
[155] "AITL..9"       "AITL..450"     "AITL..534"     "AITL..66"      "AITL..255"     "AITL..207"     "AITL..8"       "AITL..1"       "AITL..152"     "AITL..229"     "AITL..248"    
[166] "AITL..235"     "AITL..420"     "AITL..483"     "AITL..157"     "AITL..179"     "AITL..67"      "AITL..70"      "AITL..225"     "AITL..71"      "AITL..58"      "AITL..78"     
[177] "AITL..137"     "AITL..206"     "AITL..459"     "AITL..144"     "AITL..222"     "PTCL.nos..294" "AITL..198"     "AITL..453"     "AITL..210"     "AITL..26"      "AITL..114"    
[188] "PTCL.nos..226" "AITL..51"      "AITL..224"     "AITL..69"      "AITL..123"     "AITL..221"     "AITL..150"     "AITL..43"      "AITL..153"     "AITL..520"     "AITL..159"    
[199] "AITL..79"      "AITL..204"     "AITL..214"     "PTCL.nos..246" "AITL..167"     "AITL..190"     "PTCL.nos..289" "PTCL.nos..287" "PTCL.nos..288" "PTCL.nos..128" "PTCL.nos..136"
[210] "PTCL.nos..91"  "PTCL.nos..86"  "PTCL.nos..196" "PTCL.nos..212" "PTCL.nos..13"  "PTCL.nos..96"  "PTCL.nos..95"  "PTCL.nos..208" "PTCL.nos..290" "PTCL.nos..115" "PTCL.nos..219"
[221] "AITL..484"     "AITL..7"       "AITL..457"     "AITL..454"     "PTCL.nos..200" "PTCL.nos..215" "PTCL.nos..252" "PTCL.nos..166" "PTCL.nos..218" "PTCL.nos..291" "PTCL.nos..292"
[232] "PTCL.nos..467" "PTCL.nos..482" "AITL..502"     "AITL..515"     "AITL..406"     "PTCL.nos..162" "PTCL.nos..465" "PTCL.nos..203" "PTCL.nos..63"  "PTCL.nos..240" "PTCL.nos..33" 
[243] "AITL..510"     "AITL..513"     "AITL..147"     "AITL..426"     "PTCL.nos..527" "PTCL.nos..414" "PTCL.nos..415" "AITL..14"      "AITL..456"     "AITL..205"     "AITL..55"     
[254] "AITL..64"      "AITL..199"     "AITL..65"      "PTCL.nos..3"   "AITL..121"     "AITL..517"     "PTCL.nos..174" "PTCL.nos..524" "PTCL.nos..243" "PTCL.nos..242" "PTCL.nos..244"
[265] "AITL..392"     "AITL..466"     "AITL..4"       "AITL..5"       "PTCL.nos..241" "AITL..417"     "AITL..461"    
cluster.pts.nr$tree_col$labels 
  [1] "AITL..1"       "AITL..10"      "AITL..106"     "AITL..11"      "AITL..110"     "AITL..113"     "AITL..114"     "AITL..12"      "AITL..121"     "AITL..123"     "AITL..127"    
 [12] "AITL..129"     "AITL..133"     "AITL..137"     "AITL..14"      "AITL..144"     "AITL..147"     "AITL..150"     "AITL..152"     "AITL..153"     "AITL..154"     "AITL..157"    
 [23] "AITL..159"     "AITL..163"     "AITL..165"     "AITL..167"     "AITL..17"      "AITL..179"     "AITL..18"      "AITL..187"     "AITL..19"      "AITL..190"     "AITL..191"    
 [34] "AITL..197"     "AITL..198"     "AITL..199"     "AITL..2"       "AITL..204"     "AITL..205"     "AITL..206"     "AITL..207"     "AITL..210"     "AITL..214"     "AITL..221"    
 [45] "AITL..222"     "AITL..223"     "AITL..224"     "AITL..225"     "AITL..229"     "AITL..234"     "AITL..235"     "AITL..238"     "AITL..248"     "AITL..250"     "AITL..255"    
 [56] "AITL..256"     "AITL..257"     "AITL..259"     "AITL..26"      "AITL..260"     "AITL..392"     "AITL..4"       "AITL..406"     "AITL..411"     "AITL..413"     "AITL..417"    
 [67] "AITL..419"     "AITL..420"     "AITL..426"     "AITL..43"      "AITL..435"     "AITL..438"     "AITL..44"      "AITL..449"     "AITL..45"      "AITL..450"     "AITL..453"    
 [78] "AITL..454"     "AITL..456"     "AITL..457"     "AITL..458"     "AITL..459"     "AITL..461"     "AITL..466"     "AITL..472"     "AITL..473"     "AITL..479"     "AITL..481"    
 [89] "AITL..483"     "AITL..484"     "AITL..487"     "AITL..5"       "AITL..502"     "AITL..505"     "AITL..51"      "AITL..510"     "AITL..513"     "AITL..515"     "AITL..517"    
[100] "AITL..518"     "AITL..520"     "AITL..523"     "AITL..530"     "AITL..532"     "AITL..534"     "AITL..536"     "AITL..55"      "AITL..58"      "AITL..6"       "AITL..60"     
[111] "AITL..62"      "AITL..64"      "AITL..65"      "AITL..66"      "AITL..67"      "AITL..69"      "AITL..7"       "AITL..70"      "AITL..71"      "AITL..74"      "AITL..77"     
[122] "AITL..78"      "AITL..79"      "AITL..8"       "AITL..82"      "AITL..84"      "AITL..9"       "PTCL.nos..100" "PTCL.nos..101" "PTCL.nos..102" "PTCL.nos..104" "PTCL.nos..105"
[133] "PTCL.nos..109" "PTCL.nos..112" "PTCL.nos..115" "PTCL.nos..118" "PTCL.nos..119" "PTCL.nos..120" "PTCL.nos..124" "PTCL.nos..126" "PTCL.nos..128" "PTCL.nos..13"  "PTCL.nos..134"
[144] "PTCL.nos..135" "PTCL.nos..136" "PTCL.nos..139" "PTCL.nos..143" "PTCL.nos..15"  "PTCL.nos..151" "PTCL.nos..156" "PTCL.nos..16"  "PTCL.nos..161" "PTCL.nos..162" "PTCL.nos..166"
[155] "PTCL.nos..171" "PTCL.nos..174" "PTCL.nos..178" "PTCL.nos..180" "PTCL.nos..185" "PTCL.nos..186" "PTCL.nos..189" "PTCL.nos..194" "PTCL.nos..195" "PTCL.nos..196" "PTCL.nos..20" 
[166] "PTCL.nos..200" "PTCL.nos..201" "PTCL.nos..203" "PTCL.nos..208" "PTCL.nos..209" "PTCL.nos..211" "PTCL.nos..212" "PTCL.nos..213" "PTCL.nos..215" "PTCL.nos..216" "PTCL.nos..218"
[177] "PTCL.nos..219" "PTCL.nos..22"  "PTCL.nos..226" "PTCL.nos..23"  "PTCL.nos..230" "PTCL.nos..231" "PTCL.nos..232" "PTCL.nos..236" "PTCL.nos..237" "PTCL.nos..239" "PTCL.nos..24" 
[188] "PTCL.nos..240" "PTCL.nos..241" "PTCL.nos..242" "PTCL.nos..243" "PTCL.nos..244" "PTCL.nos..246" "PTCL.nos..247" "PTCL.nos..249" "PTCL.nos..25"  "PTCL.nos..251" "PTCL.nos..252"
[199] "PTCL.nos..258" "PTCL.nos..27"  "PTCL.nos..28"  "PTCL.nos..287" "PTCL.nos..288" "PTCL.nos..289" "PTCL.nos..29"  "PTCL.nos..290" "PTCL.nos..291" "PTCL.nos..292" "PTCL.nos..293"
[210] "PTCL.nos..294" "PTCL.nos..3"   "PTCL.nos..30"  "PTCL.nos..32"  "PTCL.nos..33"  "PTCL.nos..34"  "PTCL.nos..408" "PTCL.nos..409" "PTCL.nos..414" "PTCL.nos..415" "PTCL.nos..416"
[221] "PTCL.nos..424" "PTCL.nos..428" "PTCL.nos..432" "PTCL.nos..434" "PTCL.nos..440" "PTCL.nos..441" "PTCL.nos..444" "PTCL.nos..445" "PTCL.nos..446" "PTCL.nos..448" "PTCL.nos..451"
[232] "PTCL.nos..452" "PTCL.nos..455" "PTCL.nos..46"  "PTCL.nos..460" "PTCL.nos..463" "PTCL.nos..465" "PTCL.nos..467" "PTCL.nos..468" "PTCL.nos..469" "PTCL.nos..47"  "PTCL.nos..470"
[243] "PTCL.nos..471" "PTCL.nos..482" "PTCL.nos..504" "PTCL.nos..506" "PTCL.nos..519" "PTCL.nos..52"  "PTCL.nos..524" "PTCL.nos..527" "PTCL.nos..529" "PTCL.nos..531" "PTCL.nos..535"
[254] "PTCL.nos..61"  "PTCL.nos..63"  "PTCL.nos..68"  "PTCL.nos..72"  "PTCL.nos..80"  "PTCL.nos..86"  "PTCL.nos..87"  "PTCL.nos..89"  "PTCL.nos..90"  "PTCL.nos..91"  "PTCL.nos..92" 
[265] "PTCL.nos..93"  "PTCL.nos..94"  "PTCL.nos..95"  "PTCL.nos..96"  "PTCL.nos..97"  "PTCL.nos..98"  "PTCL.nos..99" 
#pheatmap::pheatmap(test, filename="test.pdf")

LOOCV on AILT, PTCLnos based on 16-gene model

y = t(mat)
cl.orig = c()
for (u in 1:nrow(y)) cl.orig [u] = unlist(strsplit(rownames(y)[u],"\\."))[1]
perm.mother = rownames(y)
perm.son = combn (perm.mother, length(perm.mother)-1)
output <- cbind(perm.mother, NA)
for (i in 1:length(perm.mother)) {
  train <- y [ perm.son[,i], ]
  test <- y [ ! ( rownames(y) %in% perm.son[,i]) , ]
  cl <- cl.orig [which(rownames(y)%in%perm.son[,i])]
  z <- lda(train, cl)
  p <- predict(z,test)$class
  output  [ setdiff(1:271, which( rownames(y) %in% perm.son[,i]) ) , 2  ] = as.character(p)
#  output  [ output[,1] == rownames(test) , 3  ] = z$scaling [1,1]
#  output  [ output[,1] == rownames(test) , 4  ] = z$scaling [2,1]
#  output  [ output[,1] == rownames(test) , 5  ] = z$scaling [3,1]
}
colnames(output) = c("true","LOOCV.predicted")
output = as.data.frame(output)
output$true.class = cl.orig
table(output$true.class, output$LOOCV.predicted  )
      
       AITL PTCL
  AITL  106   21
  PTCL   16  128
confusionMatrix(table(output$true.class, output$LOOCV.predicted  ))
Confusion Matrix and Statistics

      
       AITL PTCL
  AITL  106   21
  PTCL   16  128
                                         
               Accuracy : 0.8635         
                 95% CI : (0.8168, 0.902)
    No Information Rate : 0.5498         
    P-Value [Acc > NIR] : <2e-16         
                                         
                  Kappa : 0.7252         
 Mcnemar's Test P-Value : 0.5108         
                                         
            Sensitivity : 0.8689         
            Specificity : 0.8591         
         Pos Pred Value : 0.8346         
         Neg Pred Value : 0.8889         
             Prevalence : 0.4502         
         Detection Rate : 0.3911         
   Detection Prevalence : 0.4686         
      Balanced Accuracy : 0.8640         
                                         
       'Positive' Class : AITL           
                                         
# Confusion Matrix and Statistics
# 
#       
#        AITL PTCL
#   AITL  106   21
#   PTCL   16  128
#                                          
#                Accuracy : 0.8635         
#                  95% CI : (0.8168, 0.902)
#     No Information Rate : 0.5498         
#     P-Value [Acc > NIR] : <2e-16         
#                                          
#                   Kappa : 0.7252         
#  Mcnemar's Test P-Value : 0.5108         
#                                          
#             Specificity : 0.8591         
#          Pos Pred Value : 0.8346         
#          Neg Pred Value : 0.8889         
#              Prevalence : 0.4502         
#          Detection Rate : 0.3911         
#    Detection Prevalence : 0.4686         
#       Balanced Accuracy : 0.8640         
#                                          
#        'Positive' Class : AITL      

Use ConsensusClusterPlus to extract most significant clusters: analyze sample stratification based on the extracted differentially expressed genes betwee AILT and PTCL-nos and the ALCL ALK-negative 3-gene model.

select_hist<- pts.info.data[pts.info.data$final.molec == "AITL" | pts.info.data$final.molec == "PTCL.nos" | pts.info.data$final.molec == "ALCL.neg",]
# Add three classifier genes for ALCL ALK-neg [Agnelli et al, Blood, 2012]
# Check on array
anaplastic_gene<- c("TNFRSF8","BATF3","TMOD1")
geneannotation2 <- getBM( attributes = c("entrezgene", "external_gene_name"), filters = "external_gene_name", values = anaplastic_gene, mart = ensembl )
anaplastic_gene_ARRAY<- paste0(geneannotation2$entrezgene, "_at")
# Append 16-gene model to 3-gene model
list_genes_all<- c(list_genes, anaplastic_gene_ARRAY)
# Redo consensus cluster analysis
gep<- geneExpr[,select_hist$sample.nameNEW]
mat<- gep[list_genes_all,]
title=tempdir()
d<- data.matrix(mat)
d = sweep(d,1, apply(d,1,median,na.rm=T))
results = ConsensusClusterPlus(d,maxK=8,
                               pFeature=1,
                               title=title,
                               clusterAlg="hc",
                               innerLinkage="ward.D2",
                               finalLinkage="ward.D2",
                               distance="euclidean",
                               seed=123456789)
end fraction

kk<- as.data.frame((results[[5]]$consensusClass)) ##### 4 significant cluster
kk$geo.id<- rownames(kk)
colnames(kk)[1]<- "cluster"
table(kk$cluster)

  1   2   3   4   5 
 87 103  32  63  55 

Plot heatmap AITL, PTCL-NOS, ALCL-neg and the 19-gene model

heat<- merge(t(mat), kk, by.x = 0, by.y="geo.id")
heat2<- merge(heat, pts.info.data, by.x = 1, by.y="sample.nameNEW")
heat2<- heat2[order(heat2$cluster),]
mycol= c("red","white","yellow")
mylabel = heat2[,c("Row.names","cluster","final.molec","TET2","RHOA","IDH2","DNMT3A")]
colnames(mylabel)<- c("sample.names","clusters","Histology","TET2","RHOA","IDH2","DNMT3A")
rownames(mylabel) = mylabel$sample.names
mylabel$sample.names  = NULL
mylabel.nocol = mylabel
mylabel.col = mylabel
mylabel.col[is.na(mylabel.col)]<-0
#head(mylabel.col)
mylabel.col$Histology[mylabel.col$Histology == "AITL"] = "black"; mylabel.col$Histology[mylabel.col$Histology == "PTCL.nos"] = "orange"; mylabel.col$Histology[mylabel.col$Histology == "ALCL.neg"] = "yellow"
for (a in c(3:6)) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol )
mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Set2"))
for (a in 1) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol_plus[1:5] )
mylabel.nocol$clusters<-as.numeric(as.character(mylabel.nocol$clusters))
mylabel.nocol$clusters<-as.character(paste("cluster",mylabel.nocol$clusters, sep=""))
  
par(mfrow=c(1,1))
par(mar=c(5,5,5,5), xpd=F)
mat3<- t(data.matrix(heat2[,2:20]))
colnames(mat3)<-heat2$Row.names
mat3= mat3[order(rownames(mat3)),]
temp_name = getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",rownames(mat3)), mart = ensembl)[,c(2:3)]
temp_name = temp_name[!duplicated(temp_name[,1]),]
rownames(mat3) = temp_name$external_gene_name
mat3  <- mat3 - rowMeans(mat3)
par(mfrow=c(1,1))
pheatmap(mat3, annotation_col = mylabel.nocol, annotation_colors = list(clusters = c(cluster1= mycol_plus[1], cluster2 = mycol_plus[2], cluster3 = mycol_plus[3], cluster4 = mycol_plus[4], cluster5 = mycol_plus[5]), Histology = c(AITL = "black", PTCL.nos = "orange", ALCL.neg= "yellow"), IDH2 = c(MUT=mycol[1], "NA"=mycol[2],WT=mycol[3]), RHOA = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]), TET2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]), DNMT3A = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]) ) ,  border_color= NA, color = colorRampPalette(rev(brewer.pal(n = 5 , name = "RdYlBu")))(100), scale = "row", cluster_cols = FALSE, show_colnames= F, row_annotation =3, cellheight = 20)
#dev.off()
gep<- geneExpr[,select_hist$sample.nameNEW]
mat<- gep[list_genes_all,]
geneannotation1 <- getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",list_genes_all), mart = ensembl)
sort(unique(geneannotation1$external_gene_name))
 [1] "ADRA2A"     "AL441992.1" "ARHGEF10"   "BATF3"      "C3"         "COL4A4"     "DZIP1"      "EFNB2"      "HS3ST3A1"   "ID2"        "NETO2"      "OSMR"       "PRRX1"     
[14] "ROBO1"      "SLC5A3"     "TMOD1"      "TNFRSF8"    "XKR4"       "YAP1"      
setdiff(rownames(mat), paste0(unique(geneannotation1$entrezgene),"_at"))
character(0)
for (ii in 1:nrow(mat)) {
  #if(length (which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])) != 0 ) rownames(mat) [ii] = geneannotation1$external_gene_name [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
  rownames(mat) [ii] = unique(geneannotation1$external_gene_name) [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
}
mycol= c("red","white","yellow")
mylabel = select_hist[,c("sample.nameNEW","final.molec","IDH2","RHOA","TET2","DNMT3A")]
rownames(mylabel) = mylabel$sample.nameNEW
mylabel$sample.nameNEW = NULL
mylabel.nocol = mylabel
mylabel.col = mylabel
mylabel.col[is.na(mylabel.col)]<-0
#head(mylabel.col)
mylabel.col$final.molec[mylabel.col$final.molec == "AITL"] = "black"; mylabel.col$final.molec[mylabel.col$final.molec == "PTCL.nos"] = "orange"; mylabel.col$final.molec[mylabel.col$final.molec == "ALCL.neg"] = "yellow"
for (a in 2:5) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol )
mat  <- mat - rowMeans(mat)
par(mfrow=c(1,1))

pheatmap(mat, annotation_col = mylabel.nocol, annotation_colors = list(final.molec = c(AITL = "black", PTCL.nos = "orange", ALCL.neg = "yellow"), filename= "x.pdf",
                                  IDH2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  RHOA = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  TET2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  DNMT3A = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]) ) , show_colnames = F, cellheight = 15, 
         border_color= NA, color = colorRampPalette(rev(brewer.pal(n = 5 , name = "RdYlBu")))(20), scale = "row", clustering_method = "ward.D2",clustering_distance_cols = "euclidean" , silent = F)

### export pts order
cluster.pts.nr$tree_col$labels [cluster.pts.nr$tree_col$order]
  [1] "PTCL.nos..23"  "PTCL.nos..428" "PTCL.nos..448" "PTCL.nos..124" "PTCL.nos..247" "PTCL.nos..463" "PTCL.nos..89"  "PTCL.nos..156" "PTCL.nos..432" "PTCL.nos..216" "PTCL.nos..25" 
 [12] "PTCL.nos..87"  "PTCL.nos..94"  "PTCL.nos..98"  "PTCL.nos..105" "PTCL.nos..93"  "PTCL.nos..195" "AITL..413"     "PTCL.nos..531" "PTCL.nos..143" "PTCL.nos..46"  "PTCL.nos..28" 
 [23] "PTCL.nos..185" "PTCL.nos..416" "PTCL.nos..112" "PTCL.nos..424" "PTCL.nos..134" "PTCL.nos..32"  "PTCL.nos..22"  "PTCL.nos..194" "PTCL.nos..30"  "PTCL.nos..211" "PTCL.nos..52" 
 [34] "PTCL.nos..97"  "PTCL.nos..201" "PTCL.nos..27"  "PTCL.nos..68"  "PTCL.nos..139" "PTCL.nos..72"  "PTCL.nos..120" "PTCL.nos..444" "PTCL.nos..24"  "PTCL.nos..15"  "PTCL.nos..109"
 [45] "PTCL.nos..29"  "PTCL.nos..100" "PTCL.nos..171" "PTCL.nos..104" "PTCL.nos..99"  "PTCL.nos..126" "PTCL.nos..258" "AITL..536"     "PTCL.nos..535" "PTCL.nos..20"  "PTCL.nos..102"
 [56] "PTCL.nos..452" "PTCL.nos..529" "PTCL.nos..90"  "PTCL.nos..230" "PTCL.nos..231" "PTCL.nos..232" "PTCL.nos..236" "PTCL.nos..16"  "PTCL.nos..189" "PTCL.nos..506" "PTCL.nos..519"
 [67] "PTCL.nos..151" "PTCL.nos..186" "AITL..419"     "PTCL.nos..455" "PTCL.nos..47"  "PTCL.nos..213" "PTCL.nos..161" "PTCL.nos..61"  "PTCL.nos..209" "PTCL.nos..119" "PTCL.nos..80" 
 [78] "PTCL.nos..34"  "PTCL.nos..440" "AITL..19"      "AITL..18"      "PTCL.nos..504" "PTCL.nos..118" "PTCL.nos..293" "PTCL.nos..92"  "PTCL.nos..251" "PTCL.nos..101" "PTCL.nos..446"
 [89] "AITL..479"     "PTCL.nos..469" "AITL..411"     "AITL..473"     "AITL..472"     "AITL..481"     "AITL..487"     "PTCL.nos..434" "PTCL.nos..180" "PTCL.nos..135" "PTCL.nos..445"
[100] "PTCL.nos..408" "PTCL.nos..409" "PTCL.nos..460" "PTCL.nos..468" "PTCL.nos..470" "PTCL.nos..471" "PTCL.nos..441" "PTCL.nos..451" "AITL..12"      "PTCL.nos..237" "AITL..165"    
[111] "PTCL.nos..178" "AITL..458"     "AITL..191"     "AITL..163"     "AITL..187"     "AITL..62"      "PTCL.nos..249" "AITL..250"     "AITL..257"     "AITL..110"     "AITL..260"    
[122] "AITL..60"      "AITL..77"      "AITL..84"      "AITL..106"     "AITL..74"      "AITL..133"     "AITL..113"     "AITL..127"     "AITL..44"      "AITL..82"      "AITL..197"    
[133] "AITL..223"     "AITL..17"      "AITL..523"     "AITL..530"     "AITL..154"     "AITL..45"      "AITL..505"     "AITL..2"       "AITL..238"     "AITL..11"      "AITL..259"    
[144] "AITL..10"      "AITL..234"     "AITL..435"     "PTCL.nos..239" "AITL..6"       "AITL..438"     "AITL..518"     "AITL..532"     "AITL..256"     "AITL..449"     "AITL..129"    
[155] "AITL..9"       "AITL..450"     "AITL..534"     "AITL..66"      "AITL..255"     "AITL..207"     "AITL..8"       "AITL..1"       "AITL..152"     "AITL..229"     "AITL..248"    
[166] "AITL..235"     "AITL..420"     "AITL..483"     "AITL..157"     "AITL..179"     "AITL..67"      "AITL..70"      "AITL..225"     "AITL..71"      "AITL..58"      "AITL..78"     
[177] "AITL..137"     "AITL..206"     "AITL..459"     "AITL..144"     "AITL..222"     "PTCL.nos..294" "AITL..198"     "AITL..453"     "AITL..210"     "AITL..26"      "AITL..114"    
[188] "PTCL.nos..226" "AITL..51"      "AITL..224"     "AITL..69"      "AITL..123"     "AITL..221"     "AITL..150"     "AITL..43"      "AITL..153"     "AITL..520"     "AITL..159"    
[199] "AITL..79"      "AITL..204"     "AITL..214"     "PTCL.nos..246" "AITL..167"     "AITL..190"     "PTCL.nos..289" "PTCL.nos..287" "PTCL.nos..288" "PTCL.nos..128" "PTCL.nos..136"
[210] "PTCL.nos..91"  "PTCL.nos..86"  "PTCL.nos..196" "PTCL.nos..212" "PTCL.nos..13"  "PTCL.nos..96"  "PTCL.nos..95"  "PTCL.nos..208" "PTCL.nos..290" "PTCL.nos..115" "PTCL.nos..219"
[221] "AITL..484"     "AITL..7"       "AITL..457"     "AITL..454"     "PTCL.nos..200" "PTCL.nos..215" "PTCL.nos..252" "PTCL.nos..166" "PTCL.nos..218" "PTCL.nos..291" "PTCL.nos..292"
[232] "PTCL.nos..467" "PTCL.nos..482" "AITL..502"     "AITL..515"     "AITL..406"     "PTCL.nos..162" "PTCL.nos..465" "PTCL.nos..203" "PTCL.nos..63"  "PTCL.nos..240" "PTCL.nos..33" 
[243] "AITL..510"     "AITL..513"     "AITL..147"     "AITL..426"     "PTCL.nos..527" "PTCL.nos..414" "PTCL.nos..415" "AITL..14"      "AITL..456"     "AITL..205"     "AITL..55"     
[254] "AITL..64"      "AITL..199"     "AITL..65"      "PTCL.nos..3"   "AITL..121"     "AITL..517"     "PTCL.nos..174" "PTCL.nos..524" "PTCL.nos..243" "PTCL.nos..242" "PTCL.nos..244"
[265] "AITL..392"     "AITL..466"     "AITL..4"       "AITL..5"       "PTCL.nos..241" "AITL..417"     "AITL..461"    

Cibersort algorithm to characterize the tumour and microenviroment composition of each cluster

##### cibersort and origical molecular histologies
load("./Rmd.files/cibersort.all.Rdata")
ciber_all<-as.data.frame.matrix(t(cibersort.percentages))
ciber_all$sample.nameNEW <- rownames(ciber_all)
colnames(kk)[2]<-"sample.nameNEW"
require(plyr)
final <-join(ciber_all, kk, by = "sample.nameNEW",  type="left")
final2<-merge(pts.info.data[,c(1,6,14:17)], final, by="sample.nameNEW")
final3<- subset(final2, final.molec %in% c("AITL","ALCL.neg","ALCL.pos","ATLL","NKT","PTCL.nos"))
final3<- final3[order(final3$final.molec),]
library(RColorBrewer)
n <- 22
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
par(mar=c(2,5,7,10), xpd=TRUE)
x<- barplot(t(final3[7:28]), names.arg = rep("", length(final3$final.molec)), cex.names = 0.7, col=col_vector, border=NA,
            space=rep(0, nrow(final3)))
legend("topright",legend=colnames(final3)[7:28], col=col_vector, pch=c(15), inset=c(-0.11,0), pt.cex= 1,
cex = 1, bty = "n",  x.intersp = 0.7)
names_hist<- unique(final3$final.molec)
col_hist<- c("orange","yellow","dodgerblue2","brown2","darkorchid1","black")
num<- as.numeric(table(final3$final.molec))
for(i in (1:length(num)))
{
  segments(x[sum(num[1:i])+1-num[i]], 1.05,x[sum(num[1:i])],1.05,lwd=4, col=col_hist[i])
  text(x[(sum(num[1:i])-num[i] +1+ sum(num[1:i]))/2], 1.1, names_hist[i], cex=1.2, srt=0)
}



##### plot cibersort profile of patients stratified according to histology and clusters

for(i in (1:nrow(final3)))
{
final3$cluster[i][is.na(final3$cluster[i])]<- final3$final.molec[i]
}

final3$cluster <- factor(final3$cluster, levels = c( "1","2","4","NKT","3","5","ALCL.pos", "ATLL"))

final3<- final3[order(final3$cluster),]

#pdf("barplot_cibersort.pdf", width = 20, height = 7)
par(mar=c(2,5,7,10), xpd=TRUE)
x<- barplot(t(final3[7:28]), names.arg = rep("", length(final3$final.molec)), cex.names = 0.7, col=col_vector, border=NA,
            space=rep(0, nrow(final3)))
legend("topright",legend=colnames(final3)[7:28], col=col_vector, pch=c(15), inset=c(-0.11,0), pt.cex= 1,
cex = 1, bty = "n",  x.intersp = 0.7)

mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Dark2"))
names_hist<- c("C-1","C-2", "C-4","NKT","C-3","C-5","ALCL.pos","ATLL")
col_hist<- c(mycol_plus[1],mycol_plus[2],mycol_plus[4],"darkorchid1",mycol_plus[3],mycol_plus[5],"dodgerblue2","brown2","")
num<- as.numeric(table(final3$cluster))
  par(new=TRUE)
for(i in (1:(length(num))))
{
  segments(x[sum(num[1:i])+1-num[i]], 1.05,x[sum(num[1:i])],1.05,lwd=4, col=col_hist[i])
  text(x[(sum(num[1:i])-num[i] +1+ sum(num[1:i]))/2], 1.1, names_hist[i], cex=1.2, srt=0)

}
# dev.off()

Boxplot comparing the contribution of each cibersort signature between all extracted clusters

par(mfrow=c(1,2))
par(mar=c(3,3,3,3), xpd=F)
for(i in (7:27))
{
  #pdf(sprintf("%s_cibersort_ptcl.pdf",i), height=8, width=10)
  k<- as.numeric(final2[,i])
  table_wilk<- pairwise.wilcox.test(k,final2$cluster,p.adjust.methods = "bonferroni" )$p.value
  df_wilk <- data.frame(expand.grid(dimnames(table_wilk)),array(table_wilk))
  df_wilk2<-na.omit(df_wilk)
  df_wilk2_sig<- df_wilk2[df_wilk2$array.table_wilk.<0.05,]
  df_wilk2_sig$Var1<-as.numeric(as.character(df_wilk2_sig$Var1))
  df_wilk2_sig$Var2<-as.numeric(as.character(df_wilk2_sig$Var2))
  if(nrow(df_wilk2_sig)>0)
  {
  boxplot(k~final2$cluster, ylim=c(0,(max(k)+0.2)), main=colnames(final2)[i], cex.main=2, col=mycol_plus, las=2)
  for(j in (1:nrow(df_wilk2_sig)))
  {
    segments(df_wilk2_sig$Var1[j], max(k)-0.01+j/30, df_wilk2_sig$Var2[j],max(k)-0.01+j/30)
    p<-df_wilk2_sig$array.table_wilk.[j]
    if(p<0.00001){p2 = "<0.00001"}else{
    p2<-as.numeric(formatC(p,digits=6,format="f"))}
    pval <- paste("p =",p2,sep=" ")
    text((df_wilk2_sig$Var1[j]+ df_wilk2_sig$Var2[j])/1.9,  max(k) +j/30, pval, cex=0.8)
  }
    }
  #dev.off()   
   }

R tmod package analysis

# for convenience: reimport annotated matrix
final<- read.delim("./Rmd.files/aitl_nos_alcl_clsutering.txt",sep="\t",header = T,stringsAsFactors = F)
final2<- final[,c("Row.names","hist","cluster")]
mat<- read.delim("./Rmd.files/ensembl_annotated_matrix.txt", sep="\t", stringsAsFactors = F)
design <- model.matrix(~ 0+factor(final2$cluster)) ##### create matrix
colnames(design)<-paste0("Cluster_",c(1:5))
contrast.matrix <- makeContrasts(Cluster_2-Cluster_1, Cluster_3-Cluster_1,Cluster_4-Cluster_1, Cluster_5-Cluster_1,
                                 Cluster_3-Cluster_2, Cluster_4-Cluster_2, Cluster_5-Cluster_2,
                                 Cluster_4-Cluster_3, Cluster_5-Cluster_3,
                                 Cluster_4-Cluster_5,
                                 Cluster_2-(Cluster_1 + Cluster_3 + Cluster_4 + Cluster_5)/4,
                                 Cluster_3-(Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4,
                                 Cluster_4-(Cluster_1 + Cluster_2 + Cluster_3 + Cluster_5)/4,
                                 Cluster_1-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4,
                                 Cluster_5-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4,
                                 levels=design)
fit1 <- lmFit(mat, design)
fit2 <- contrasts.fit(fit1, contrast.matrix)
fit <- eBayes(fit2)
geneExpr = adj.data
geneExpr2<- geneExpr[,colnames(geneExpr) %in% final2$Row.names ]
geneExpr2<- geneExpr2[,final2$Row.names]
ensembl = useMart( "ensembl", dataset = "hsapiens_gene_ensembl" )
hgnc <- getBM(attributes=c('entrezgene','hgnc_symbol','hgnc_id'),filters = 'entrezgene', values = gsub("_at","",rownames(geneExpr2)),mart = ensembl)

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geneExpr3<- as.data.frame.matrix(geneExpr2[which(rownames(geneExpr2) %in% paste0(hgnc$entrezgene,"_at")),])
levels_design<- c("Cluster_2-Cluster_1","Cluster_3-Cluster_1","Cluster_4-Cluster_1","Cluster_5-Cluster_1",
                 "Cluster_3-Cluster_2","Cluster_4-Cluster_2","Cluster_5-Cluster_2","Cluster_4-Cluster_3",
                 "Cluster_5-Cluster_3","Cluster_4-Cluster_5",
                 "Cluster_2-(Cluster_1 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_3-(Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4",
                 "Cluster_4-(Cluster_1 + Cluster_2 + Cluster_3 + Cluster_5)/4",
                 "Cluster_1-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_5-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4")
df_diff_all=NULL
for(i in (1:length(levels_design)))
{
tt <- topTable(fit, coef=i, number=Inf, genelist=rownames(geneExpr3))
tt$ID<- rownames(tt)
colnames(tt)[1]<-"GENE_SYMBOL"
head(tt, 10)
fg <- tt$GENE_SYMBOL[tt$adj.P.Val < 0.001 & abs( tt$logFC ) > 2]
length(fg)
df_diff<- cbind(fg, rep(levels_design[i], length(fg)))
df_diff_all<-rbind(df_diff_all, df_diff)
#plot(tt$logFC, -log10(tt$adj.P.Val))
}
df_diff_all<- as.data.frame.matrix(df_diff_all)
annotation_col<- final2
colnames(annotation_col)<-c("sampleID","Hist","cluster")
A <- function(x) (as.factor(as.character(x))) ##### lapply function for all columns to generate the relative contribution
annotation_col[,1:ncol(annotation_col)] = apply(annotation_col[,1:ncol(annotation_col)], 2, function(x) as.factor(as.character(x)))
annotation_col<- as.data.frame(annotation_col[,-1])
mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Dark2"))
ann_colors = list(Hist=c( "AITL"="black","ALCL"="yellow","PTCL"="orange"),
                  cluster=c("1" = mycol_plus[1],"2" = mycol_plus[2],"3" = mycol_plus[3],"4" = mycol_plus[4],"5" =mycol_plus[5])
                  )
edata3<- mat[rownames(mat) %in% unique(df_diff_all$fg),]
pheatmap(as.matrix( as.matrix(edata3)), annotation_col=annotation_col, annotation_colors = ann_colors, border_color="NA", scale = "row", cluster_cols = FALSE, show_colnames= F, show_rownames = FALSE)

levels_design<- c("Cluster_2-Cluster_1","Cluster_3-Cluster_1","Cluster_4-Cluster_1","Cluster_5-Cluster_1",
                 "Cluster_3-Cluster_2","Cluster_4-Cluster_2","Cluster_5-Cluster_2","Cluster_4-Cluster_3",
                 "Cluster_5-Cluster_3","Cluster_4-Cluster_5",
                 "Cluster_2-(Cluster_1 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_3-(Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4",
                 "Cluster_4-(Cluster_1 + Cluster_2 + Cluster_3 + Cluster_5)/4",
                 "Cluster_1-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_5-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4")
df_diff_all=NULL
for(i in (1:length(levels_design)))
{
tt <- topTable(fit, coef=i, number=Inf, genelist=rownames(geneExpr3))
tt$ID<- rownames(tt)
colnames(tt)[1]<-"GENE_SYMBOL"
head(tt, 10)
fg <- tt$GENE_SYMBOL[tt$adj.P.Val < 0.001 & abs( tt$logFC ) > 2]
length(fg)
df_diff<- cbind(fg, rep(levels_design[i], length(fg)))
df_diff_all<-rbind(df_diff_all, df_diff)
#plot(tt$logFC, -log10(tt$adj.P.Val))
}
df_diff_all<- as.data.frame.matrix(df_diff_all)
table(df_diff_all$V2)

Cluster_1-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4 Cluster_3-(Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4                                         Cluster_3-Cluster_1 
                                                         18                                                          84                                                         203 
                                        Cluster_3-Cluster_2                                         Cluster_4-Cluster_1                                         Cluster_4-Cluster_2 
                                                        121                                                          15                                                           2 
                                        Cluster_4-Cluster_3                                         Cluster_4-Cluster_5 Cluster_5-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4 
                                                         78                                                           8                                                           5 
                                        Cluster_5-Cluster_1                                         Cluster_5-Cluster_2                                         Cluster_5-Cluster_3 
                                                         41                                                          19                                                          74 
annotation_col<- final2
colnames(annotation_col)<-c("sampleID","Hist","cluster")
A <- function(x) (as.factor(as.character(x))) ##### lapply function for all columns to generate the relative contribution
annotation_col[,1:ncol(annotation_col)] = apply(annotation_col[,1:ncol(annotation_col)], 2, function(x) as.factor(as.character(x)))
annotation_col<- as.data.frame(annotation_col[,-1])
mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Dark2"))
ann_colors = list(Hist=c( "AITL"="black","ALCL"="yellow","PTCL"="orange"),
                  cluster=c("1" = mycol_plus[1],"2" = mycol_plus[2],"3" = mycol_plus[3],"4" = mycol_plus[4],"5" =mycol_plus[5])
                  )
edata3<- mat[rownames(mat) %in% unique(df_diff_all$fg),]
pheatmap(as.matrix( as.matrix(edata3)), annotation_col=annotation_col, annotation_colors = ann_colors, border_color="NA",
         scale = "row", cluster_cols = FALSE, show_colnames= F, show_rownames = FALSE)

############ table of genes
df_diff_all_tab=NULL
for(i in (1:length(levels_design)))
{
  tt <- topTable(fit, coef=i, number=Inf, genelist=rownames(geneExpr3))
  tt$ID<- rownames(tt)
  colnames(tt)[1]<-"GENE_SYMBOL"
  head(tt,10)
  fg <- tt[tt$adj.P.Val < 0.001 & abs( tt$logFC ) > 2,]
  if(nrow(fg)>0){
    fg$design<- levels_design[i]
  df_diff_all_tab<-rbind.data.frame(df_diff_all_tab, fg)
  #plot(tt$logFC"," -log10(tt$adj.P.Val))
  }
  }
nrow(df_diff_all_tab) #### number of genes differentially expressed between C-1, C-2, C-3, C-4, C-5
[1] 668
##### list gene from Iqbal et al. blood 2014
iqbal<- unique(c("EFNB2","ROBO1","S1PR3","ANK2","LPAR1","SNAP91","SOX8","LPAR1","RAMP3","S1PR3","ROBO1","EFNB2","TUBB2B","SOX8",
                 "SOX8","ARHGEF10","DMRT1",  "SLC19A21","STK3","PERP","TNFRSF8","TMOD1","BATF3","CDC14B","PERP","WDFEY3",
                 "TMOD1","ATP6V0D1","AXL","CD59","CHI3L1","CLTC","COL6A1","CREG1","CTSB","CTSC","NR1","H3","PDXK","PITPNA",
                 "PLSCR1","PRDX3","CTSS","CYBB","FABP3","FPR1","FTL","GUCA2A","HCK","IFI30","IL13RA1","JAK2","LILRB1",
                 "PRKG1PSAP","SLC7A7","SOD2","TCN2","THY1","TYR","UBE2L6","WARS","AXL","FTL","SIRPA","STAT1","CSF2","IFNG",
                 "SEPT6","GATA3","CD28","STAT1","AXL","CD28","CD40","CD59","CSF2","FTL","IFNG","LILRB1","SIRPA","TBX21",
                 "MSH6","EGR1","CAT","EGR1","CAT"))
intersect(iqbal, unique(df_diff_all_tab$GENE_SYMBOL))
 [1] "ROBO1"    "LPAR1"    "SOX8"     "TUBB2B"   "TNFRSF8"  "TMOD1"    "BATF3"    "ATP6V0D1" "CHI3L1"   "CREG1"    "CTSB"     "CTSC"     "FTL"      "HCK"     

pairwise for pathway using tmod (https://cran.r-project.org/web/packages/tmod/vignettes/tmod.pdf)

fit1 <- lmFit(mat, design)
fit2 <- contrasts.fit(fit1, contrast.matrix)
fit <- eBayes(fit2)
res.l <- tmodLimmaTest(fit, rownames(mat))
length(res.l)
[1] 15
names(res.l)
 [1] "Cluster_2 - Cluster_1"                                         "Cluster_3 - Cluster_1"                                         "Cluster_4 - Cluster_1"                                        
 [4] "Cluster_5 - Cluster_1"                                         "Cluster_3 - Cluster_2"                                         "Cluster_4 - Cluster_2"                                        
 [7] "Cluster_5 - Cluster_2"                                         "Cluster_4 - Cluster_3"                                         "Cluster_5 - Cluster_3"                                        
[10] "Cluster_4 - Cluster_5"                                         "Cluster_2 - (Cluster_1 + Cluster_3 + Cluster_4 + Cluster_5)/4" "Cluster_3 - (Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4"
[13] "Cluster_4 - (Cluster_1 + Cluster_2 + Cluster_3 + Cluster_5)/4" "Cluster_1 - (Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4" "Cluster_5 - (Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4"
pie <- tmodLimmaDecideTests(fit, genes=rownames(mat))
par(mfrow=c(1,1))
res.l2<- lapply(res.l, function(x) {x[x$adj.P.Val<10e-8,]})
tmodPanelPlot(res.l2, pie=pie, text.cex=0.6) ##### zero = grey, blue down in the first factor and red up in the first

res.l2<- lapply(res.l, function(x) {x[x$adj.P.Val>10e-8 & x$adj.P.Val<10e-5,]})
tmodPanelPlot(res.l2, pie=pie, text.cex=0.6) ##### zero = grey, blue down in the first factor and red up in the first

---
title: "PTCL Gene Expression based classification"
author:
- affiliation: University of Milan
  name: Luca Agnelli
- affiliation: Wellcome Trust Sanger Institute, UK and University of Milan, IT
  name: Francesco Maura
date: "2018/07/18"
output:
  pdf_document: 
    toc: true
    toc_depth: 2
  html_document: 
    always_allow_html: yes
  html_notebook: default
subtitle: Integration of Transcriptional and Mutational Data Improves the Stratification
  of Peripheral T-Cell Lymphoma series
---
Built with R version:  
`r getRversion()`

---
Built with R version:  
`r getRversion()`

# Libraries

Load necessary libraries

```{r setup, include=TRUE, warning=FALSE}
library(affy)
library(ComplexHeatmap)
library(plot3D)
library(gplots)
library(circlize)
library(AnnotationDbi)
library(limma)
library(lattice)
library(org.Hs.eg.db)
library(MASS)
library(RColorBrewer)
library(AnnotationDbi)
library(rglwidget)
###library(hgu133plus2hsentrezgcdf)
library(VennDiagram)
library(org.Hs.eg.db)
library(GenomicRanges)
library(GenomicFeatures)
library(rtracklayer)
library(biomaRt)
library(glmnet)
library(survival)
library(Hmisc)
library(ConsensusClusterPlus)
library(pheatmap)
library(ggplot2)
library(heatmap.plus)
library(rgl)
library(caret)
library(e1071)
library(tmod)

set1 = c(brewer.pal(9,"Set1"), brewer.pal(8, "Dark2"))

violinJitter <- function(x, magnitude=1){
  d <- density(x)
  data.frame(x=x, y=runif(length(x),-magnitude/2, magnitude/2) * approxfun(d$x, d$y)(x))
}

rotatedLabel <- function(x0 = seq_along(labels), y0 = rep(par("usr")[3], length(labels)), labels, pos = 1, cex=1, srt=45, ...) {
  w <- strwidth(labels, units="user", cex=cex)
  h <- strheight(labels, units="user",cex=cex)
  u <- par('usr')
  p <- par('plt')
  f <- par("fin")
  xpd <- par("xpd")
  par(xpd=NA)
  text(x=x0 + ifelse(pos==1, -1,1) * w/2*cos(srt/360*2*base::pi), y = y0 + ifelse(pos==1, -1,1) * w/2 *sin(srt/360*2*base::pi) * (u[4]-u[3])/(u[2]-u[1]) / (p[4]-p[3]) * (p[2]-p[1])* f[1]/f[2] , labels, las=2, cex=cex, pos=pos, adj=1, srt=srt,...)
  par(xpd=xpd)
}


avefc = function (y, log=TRUE, replace= FALSE) {
     if (log) y = 2^y
   if (replace) y = y + (1-min(y))
   m = apply(y,1,mean)
     y.n = y/m  
     y.n2 = y.n
     y.n2 [y.n2 < 1] = 1/ (y.n2 [y.n2 < 1])
     ave.fc = apply (y.n2, 1, mean)
     return(ave.fc)
     }

```

## Ensembl Library

For  gene convertion from array to HUGO

```{r}
ensembl = useMart( "ensembl", dataset = "hsapiens_gene_ensembl" )
```

## Gene Expression Data

Upload or generate GEP normalized matrix

```{r, warning=FALSE}
### choice 1: import processed matrix
# data.dir="./Rmd.files/"
data.dir = '/Users/emagene/Dropbox/codes/github/PTCL/'
setwd(data.dir)
load (file.path(data.dir,"/Rmd.files/541_PTCL_batch_adjusted_geo.id.Rdata"))

geneExpr = adj.data
# import batch and re-order accordingly
load(file.path(data.dir,"/Rmd.files/PTCL.batch.Rdata"))
batch = batch [order(batch$nameNEW),]
batch.series = as.vector(batch$center)
batch$cancer = "cancer"

# ### OPTIONAL: CHECK BATCH ON FINAL.MOLEC
# 
# #mod = model.matrix(~as.factor(center), data=batch)
# mod = model.matrix(~as.factor(final.molec), data=design)
# mod0 = model.matrix(~1, data= batch)
# library(sva)
# n.sv = num.sv(adj.data,mod,method="leek")
# svobj = sva(adj.data,mod,mod0,n.sv=n.sv)
# 
# pValues = f.pvalue(adj.data,mod,mod0)
# qValues = p.adjust(pValues,method="BH")
# modSv = cbind(mod,svobj$sv)
# mod0Sv = cbind(mod0,svobj$sv)
# pValuesSv = f.pvalue(adj.data,modSv,mod0Sv)
# qValuesSv = p.adjust(pValuesSv,method="BH")

### end of choice 1

### choice 2: generate your own affy object and custom data

# download CEL files from GEO series GSE6338, GSE19067, GSE19069, GSE40160, GSE58445, GSE65823 and EBI series ETABM702, ETABM783
# GSM368580.CEL, GSM368582.CEL, GSM368584.CEL, GSM368586.CEL, GSM368589.CEL, GSM368591.CEL, GSM368594.CEL, GSM472164.CEL, GSM1411278.CEL, GSM1411284.CEL, GSM1411285.CEL, GSM1411287.CEL, GSM1411355.CEL, GSM1411364.CEL, GSM1411368.CEL, GSM1411425.CEL, GSM1411427.CEL excluded from the analysis (see Methods for explaination")
### celfiles <- dir("~/Documents/DATI/PTCL.nos/GSE6338-GSE19067-GSE19069-GSE40160-GSE58445-GSE65823-ETABM702-ETABM783/", pattern = ".CEL")
### library(affy)
### gset = justRMA(celfile.path = "/Users/emagene/Documents/DATI/PTCL.nos/GSE6338-GSE19067-GSE19069-GSE40160-GSE58445-GSE65823-ETABM702-ETABM783/", ### filenames = celfiles, sampleNames = gsub(".CEL","", celfiles), cdfname = "hgu133plus2hsentrezgcdf")
### geneExpr = exprs(gset)
### batch adjustment
### library(sva)  
### # import batch and re-order accordingly
### load("./Rmd.files/PTCL.batch.Rdata")
### batch = batch [order(rownames(batch)),]
### batch.series = as.vector(batch$center)
### geneExprNEW = geneExpr [ , order(colnames(geneExpr)) ]
### geneExprNEW = geneExprNEW[grep("AFFX",rownames(geneExprNEW), invert=TRUE),]
### # check order correspondence and, if correct, adjust data
### if (all(colnames(geneExprNEW) == rownames(batch))) {
###   adj.data = ComBat (geneExprNEW, batch.series, mod = NULL, par.prior = TRUE, prior.plots = TRUE)
### } else {
###   cat("Error: colnames and batch did not correspond")
### }
### geneExpr = adj.data
### colnames(geneExpr) = as.vector(batch$nameNEW)
### end of choice 2
```

## Clinical Data

Upload paz info with clinical and mutational data

```{r, warning=FALSE}
pts.info.data <- read.table("./Rmd.files/541_paz_info_MUT.txt", sep="\t", header=TRUE, check.names=FALSE, stringsAsFactors = F)
# customize colors for categories
levels(as.factor(pts.info.data$final.molec))
# "AITL"     "ALCL.neg" "ALCL.pos" "ATLL"     "NKT"      "PTCL.nos" "T.CD30"   "T.CD4"    "T.CD8"    "T.DR"     "T.reg"    "TCR-HL"  
colorz = c("black", "yellow","dodgerblue2","brown2","darkorchid1", "orange", "grey42", "grey52","grey62","grey72","grey82","grey92")
temp = split (  pts.info.data$sample.nameNEW, pts.info.data$final.molec )
colorx = colnames(geneExpr)
length(colorz)
length(temp)
for (i in 1:length(colorz)) colorx [ which(colorx %in% unlist(temp[i])) ] = colorz[i]
library(gplots)
colorx = col2hex(colorx)

### build design matrix and transform to numerical 
design <- pts.info.data[,c(1:2,6:8,14:17)]
rownames(design)<- design[,1]
design<- design[,-c(1:2)]
#design<-na.omit(design) ### select onyl patients with all mutations data available (n=53)
design$age<- as.numeric(as.character(design$age))
design$age<- design$age - median(design$age)
design[design == "WT"] <- 0
design[design == "MUT"] <- 1
design$final.molec[design$final.molec=="AITL"] <- 0
design$final.molec[design$final.molec=="PTCL.nos"] <- 1
design$final.molec[design$final.molec=="ALCL.neg"] <- 2
design$final.molec[design$final.molec=="ALCL.pos"] <- 3
design$final.molec[design$final.molec=="ATLL"] <- 4
design$final.molec[design$final.molec=="NKT"] <- 5
design$final.molec[477:541] <- 6
design$gender[design$gender=="M"] <- 1
design$gender[design$gender=="F"] <- 0
design$age = NULL
all(pts.info.data$sample.nameNEW == batch$nameNEW) 


```

# Pie Chart with Percentages

```{r, warning=FALSE}

slices <- table(pts.info.data$final.molec)
lbls <- names(table(pts.info.data$final.molec))
pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, ": ", slices, " (", pct, "%)", sep="" ) # add percents to labels
#pdf("Figure_1a_pie_plot.pdf", width = 5, height = 5)
par(mfrow=c(1,1))
par(mar=c(3,3,3,3), xpd=F)
pie(slices,labels = lbls, init.angle = 0, col=colorz, main="", cex=0.6, radius=0.8)
#dev.off()



```



# PCA

```{r,echo=TRUE, warning=FALSE}

# apply variational filter

afc2 = avefc(geneExpr, log=TRUE, replace=FALSE)
data541exprs.vf = geneExpr [afc2 >= 2, ]
dim(data541exprs.vf	)
# retry PCA on shorted gene list
data541m = t(as.matrix(data541exprs.vf))
pca<-prcomp(data541m,scale=T)
mfrow3d(nr = 1, nc = 1, sharedMouse = T)  
plot3d(pca$x,rgl.use=F,col=colorx,size=0.6,type="s")
rglwidget()
```

# Heatmap

```{r, warning=FALSE}

mat = as.matrix(data541exprs.vf)
base_mean = rowMeans(mat)
mat_scaled = t(apply(mat, 1, scale))
types = pts.info.data$final.molec
color.annot = col2hex(colorz); names (color.annot)= names(temp)  
ha = HeatmapAnnotation(df = data.frame(type = types) , col = list(type = c( color.annot ) ) )
ha@anno_list[[1]]@color_mapping@colors = col2hex(colorz)
names(ha@anno_list[[1]]@color_mapping@colors) = names(temp)
ht = Heatmap(mat_scaled, name = "expression", km = 7, clustering_method_columns = "ward.D", col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), top_annotation = ha, top_annotation_height = unit(4, "mm"), show_row_names = FALSE, show_column_names = FALSE)
column_order(ht)
```


# Check relative log expression after batch correction

```{r, warning=FALSE}
rle.custom = function (a, logged2 = TRUE, file = NULL, colorbox= NULL, labels=NULL , legend = NULL ) {
    a.m <- apply(a,1,median)
if (logged2) {
    for (i in 1:dim(a)[2]) {
         a [,i] <-  a [,i] - a.m
    }
    } else {
        for (i in 1:dim(a)[2]) {
         a [,i] <-  log (a [,i] / a.m )
    }
    }
   # png(file,10240,3840)
    par(mar=c(10,4,6,2))
    boxplot (a, ylim= c(-5,5), outline=F, col=colorbox, xlab="pts", names=labels, las=2, cex.axis = 1.5, main="RLE", xlim = c(1,600), cex.main = 5 )
    legend("bottomright",legend = c(levels(as.factor(pts.info.data$final.molec))),   
      fill = colorz, # 6:1 reorders so legend order matches graph
      title = "Legend",
      cex = 5)
  #  dev.off()

    a.c = apply(a, 2, stats::quantile)
    return(a.c)
}

#rle.medians = rle.custom(geneExpr, colorbox=colorx, file="./RLE.541.png", labels=pts.info.data$sample.nameNEW )
#plot(rle.medians[3,], type="l", xlab="pts", ylab="RLE median" )
rle.medians = rle.custom(geneExpr, colorbox=colorx, file="./RLE.541.png", labels=pts.info.data$sample.nameNEW )
plot(rle.medians[3,], type="l", xlab="pts", ylab="RLE median" )

```


## Final Gene Expression Matrix

Define design file and filter geneExpr for patients included in design data frame and

```{r, warning=FALSE}
design <- pts.info.data[,c(1:2,6:8,14:17)]
rownames(design)<- design[,1]
design<- design[,-c(1:2)]
design<-na.omit(design) ###  select onyl patients with all mutations data available (n=53)
design$age<- as.numeric(as.character(design$age))
design$age<- design$age - median(design$age)
design[design == "WT"] <- 0
design[design == "MUT"] <- 1
design$final.molec[design$final.molec=="AITL"] <- 0
design$final.molec[design$final.molec=="PTCL.nos"] <- 1
design$gender[design$gender=="M"] <- 1
design$gender[design$gender=="F"] <- 0
design$offset <- rep(1, nrow(design))
design<-design[,c(8,1:7)]

all(pts.info.data$sample.nameNEW == colnames(geneExpr)) ## check correspondence
# geneExpr = geneExpr [ , order (pts.info.data$geo.id)] ### do only to set correspondence in case of custom procedure
# colnames(geneExpr) = pts.info.data$sample.nameNEW [ order (pts.info.data$geo.id)]

geneExpr2<- (geneExpr[, rownames(design)])
geneExpr2<- data.matrix(geneExpr2, rownames.force = NA)
design<- data.matrix(design, rownames.force = NA)
```

## Model fitting
We use the lmFit function from the limma package. This comes with a whole series of powerful and reliable tests.

```{r, warning=FALSE}
glm = lmFit(geneExpr2[,rownames(design)], design = design )
glm = eBayes(glm)
F.stat <- classifyTestsF(glm[,-1],fstat.only=TRUE)
glm$F <- as.vector(F.stat)
df1 <- attr(F.stat,"df1")
df2 <- attr(F.stat,"df2")
if(df2[1] > 1e6){
  glm$F.p.value <- pchisq(df1*glm$F,df1,lower.tail=FALSE)
}else
  glm$F.p.value <- pf(glm$F,df1,df2,lower.tail=FALSE)

set.seed(12345678)
rlm <- lmFit(geneExpr[,rownames(design)], apply(design, 2, sample))
rlm <- eBayes(rlm)
F.stat <- classifyTestsF(rlm[,-1],fstat.only=TRUE)
rlm$F <- as.vector(F.stat)
df1 <- attr(F.stat,"df1")
df2 <- attr(F.stat,"df2")
if(df2[1] > 1e6){
  rlm$F.p.value <- pchisq(df1*rlm$F,df1,lower.tail=FALSE)
}else
  rlm$F.p.value <- pf(rlm$F,df1,df2,lower.tail=FALSE)
F.stat <- classifyTestsF(glm[,2:5],fstat.only=TRUE)
df1 <- attr(F.stat,"df1")
df2 <- attr(F.stat,"df2")
F.p.value <- pchisq(df1*F.stat,df1,lower.tail=FALSE)
R.stat <- classifyTestsF(rlm[,2:5],fstat.only=TRUE)
Rall = 1 - 1/(1 + glm$F * (ncol(design)-1)/(nrow(design)-ncol(design)))
Rgenetics = 1 - 1/(1 + F.stat * 4/(nrow(design)-ncol(design)))
Pgenetics = 1 - 1/(1 + R.stat * 4/(nrow(design)-ncol(design)))
names(Rgenetics) <- names(Pgenetics) <- names(Rall) <-  rownames(geneExpr)

```

## Differentially Expressed Genes

```{r fig.width = 10, fig.height = 7, warning=FALSE}
par(bty="n", mgp = c(2,.33,0), mar=c(3,2.5,1,1)+.1, las=1, tcl=-.25, xpd=NA)
d <- density(Pgenetics,bw=1e-3)
f <- 40 #nrow(gexpr)/512

#pdf("Figure_2a_MAY.pdf", width = 10, height = 7)
par(mfrow=c(1,1))
par(mar=c(8,5,5,5), xpd=F)
plot(d$x, d$y * f, col='grey', xlab=expression(paste("Explained variance per gene ", R^2)), main="", lwd=2, type="l", ylab="", xlim=c(0,1), cex.axis=1.2, cex.lab=1.5, bty="n")
title(ylab="Density", line=2.5, cex.lab=1.5)
d <- density(Rgenetics, bw=1e-3)
r <- min(Rgenetics[p.adjust(F.p.value,"BH")<0.01]) ######## threshold to select 412 genes
x0 <- which(d$x>r)
polygon(d$x[c(x0[1],x0)], c(0,d$y[x0])* f, col=paste(set1[1],"44",sep=""), border=NA)
lines(d$x, d$y* f, col=set1[1], lwd=2)
text(d$x[x0[1]], d$y[x0[1]]*f +20, pos=4, paste(sum(Rgenetics > r), "genes q < 0.01"))
legend("topright", bty="n", col=c(set1[1], "grey"), lty=1, c("Observed","Random"), lwd=2)
#dev.off()

glmPrediction <- glm$coefficients %*% t(design)
rlmPrediction <- rlm$coefficients %*% t(design)
```

Print signficiant genes

```{r}

kk<-as.data.frame((p.adjust(F.p.value,"BH")<0.01))
kk$gene<- rownames(kk)
colnames(kk)[1]<-"code"
kk2<-kk[kk$code=="TRUE",]
### sort(kk2$gene) ##### if you want to print the entire list of differentially expressed genes

```

## Significant effects per covariate

Extract the list of differentially expressed genes by mutations

```{r}
### customize colors in colMutations
# colMutations = c(brewer.pal(8,"Set1")[-6], rev(brewer.pal(8,"Dark2")), brewer.pal(7,"Set2"))[c(1:12,16:19,13:15)]
# o <- order(apply(col2rgb(colMutations),2,rgb2hsv)[1,])
# colMutations <- colMutations[rev(o)][(4*1:19 +15) %% 19 + 1][1:7]
colMutations = col2hex(c("magenta", "purple","gray60","red","lightblue","green","orange"))
names(colMutations) <- colnames(design)[-1]

gene_code<- kk2$gene
tab=NULL
for(i in (1:length(kk2$gene)))
{
  gene_single<- gene_code[i]
  y <- glm$coefficients[gene_single,-1]+glm$coefficients[gene_single,1]
  w <- glm$p.value[gene_single,-1] < 0.05
  int<-c(gene_single, as.character(w))
  tab<- rbind(tab, int)
}
rownames(tab)<-seq(1:nrow(tab))
colnames(tab)<- c("gene",colnames(design)[-1])

# Write to disk a file with all significant genes
#write.table(tab, "table_differentially_expressed_gene.txt",sep="\t", quote=F, row.names = F, col.names = T)
```

Example of extraction

```{r fig.width = 10, fig.height = 7}

 # temp_name = unique(getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",gene_single),
 # mart = ensembl)$external_gene_name)
  #pdf("Figure_2b.pdf", width = 10, height = 7)
  par(mfrow=c(1,1))
  par(mar=c(10,8,5,5), xpd=F)
  par(bty="n", mgp = c(1.5,.33,0),las=1, tcl=-.25, xpd=F)
  temp_name<- "YAP1"
  plot(glmPrediction[gene_single,], geneExpr[gene_single,rownames(design)], ylab="", xlab="",
       pch=16, cex=1, cex.axis=1.2, cex.lab=1.5)
  title(ylab=(paste("Observed ",temp_name, " expression")), line=2.5, cex.lab=1.5)
   title( xlab=(paste("Predicted ",temp_name, " expression")), line=2.5, cex.lab=1.5)
  abline(0,1)
  u <- par("usr")
  par(xpd=NA)
  y <- glm$coefficients[gene_single,-1]+glm$coefficients[gene_single,1]
  u <- par("usr")
  x0 <- rep(u[3]+1,ncol(design)-1)
  y0 <- u[4] + 0.05*(u[4]-u[3]) - rank(-y)/length(y) * (u[4]-u[3])/1.2
  d <- density(y)
  lines(d$x, d$y/5+1+u[3], col="grey")
  lines(d$x, -d$y/5+1+u[3], col="grey")
points(x=y, y=x0+violinJitter(y, magnitude=0.25)$y, col=colMutations, pch=16, cex=1.5)
  text(x=glm$coefficients[gene_single,1], y= 5.2, "Model coefficients", cex=0.8)
legend("topleft",names(colMutations), col = colMutations, bty= "n", cex = 1.2, pch = 16)
#dev.off()
```


Plot significant effects per covariate (q<0.01)

```{r, warning=FALSE}
testResults <- decideTests(glm, method="hierarchical",adjust.method="BH", p.value=0.01)[,-1]
significantGenes <- sapply(1:ncol(testResults), function(j){
  c <- glm$coefficients[testResults[,j]!=0,j+1]
  table(cut(c, breaks=c(-5,seq(-1.5,1.5,l=7),5)))
})

colnames(significantGenes) <- colnames(testResults)
rownames(tab)<-c(1:nrow(tab))
tab2<- as.data.frame(tab)
tab2$gene<-as.character(as.character(tab2$gene))
tab2$final.molec<-as.character(as.character(tab2$final.molec))
tab2$TET2<-as.character(as.character(tab2$TET2))
tab2$RHOA<-as.character(as.character(tab2$RHOA))
tab2$IDH2<-as.character(as.character(tab2$IDH2))
tab2$DNMT3A<-as.character(as.character(tab2$DNMT3A))


#  pdf("Figure_2c.pdf", width = 10, height = 7)
  par(mfrow=c(1,1))
  par(mar=c(8,8,5,5), xpd=F)

par(mfrow=c(1,1))
par(bty="n", mgp = c(2.5,.33,0), mar=c(5,5.5,5,0)+.1, las=2, tcl=-.25)
b <- barplot(significantGenes, las=2, ylab = "Differentially expressed genes", col=brewer.pal(8,"RdYlBu"), legend.text=FALSE , border=0, xaxt="n", cex.lab=1.5)#, col = set1[simple.annot[names(n)]], border=NA)
rotatedLabel(x0=b-0.1, y0=rep(-0.5, ncol(significantGenes)), labels=colnames(significantGenes), cex=1.2, srt=45, font=ifelse(grepl("[[:lower:]]", colnames(design))[-1], 1,3), col=colMutations)
rotatedLabel(b-0.1, colSums(significantGenes), colSums(significantGenes), pos=3, cex=, srt=45)#dev.off()
clip(0,30,0,1000)
x0 <- 7.5
image(x=x0+c(0,0.8), y=par("usr")[4]+seq(-1,1,l=9) -4, z=matrix(1:8, ncol=8), col=brewer.pal(8,"RdYlBu"), add=TRUE)
text(x=x0+1.1, y=par("usr")[4]+c(-1,0,1) -4, format(seq(-1,1,l=3),2), cex=0.66)
lines(x=rep(x0+0.9, 2), y=par("usr")[4]+c(-1,1) -4)
segments(x0+0.9,par("usr")[4] + 1-4,x0+0.95,par("usr")[4] + 1-4)
segments(x0+0.9,par("usr")[4] + 0-4,x0+0.95,par("usr")[4] + 0-4)
segments(x0+0.9,par("usr")[4] + -1-4,x0+0.95,par("usr")[4] + -1-4)
text(x0 + 0.45, par("usr")[4] + 1.5-4, "log2 FC", cex=.66)

#dev.off()

# par(bty="n", mgp = c(2.5,.33,0), mar=c(3,3.3,3,0)+.1, las=1, tcl=-.25)
# t <- table(rowSums(abs(testResults[,1:6])))
# b <- barplot(t[-1],ylab="Differentially expressed genes", col=rev(brewer.pal(7, "Spectral")[-(4:5)]), border=NA)
# rotatedLabel(b-0.1, t[-1], t[-1], pos=3, cex=1, srt=45)
# title(xlab="Associated drivers", line=2)



```

Print the list of differently expressed genes using the Ensembl annotation

```{r}
select_hist<- pts.info.data[pts.info.data$final.molec == "AITL" |  pts.info.data$final.molec == "PTCL.nos",]
gene<- as.data.frame(testResults)
sig_genes<- gene[gene$final.molec!= 0 |gene$IDH2 != 0 | gene$TET2 != 0 | gene$DNMT3A != 0 | gene$RHOA != 0,]
list_genes<-sort(rownames(sig_genes)) ##### list of signficiant genes
geneannotation1 <- getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",list_genes), mart = ensembl)
sort(unique(geneannotation1$external_gene_name))


```

Generate a heatmap with AITL, PTCL-NOS with the extracted differentially expressed genes.

```{r fig.width = 10, fig.height = 8, warning=FALSE}
gep<- geneExpr[,select_hist$sample.nameNEW]
mat<- gep[list_genes,]

setdiff(rownames(mat), paste0(unique(geneannotation1$entrezgene),"_at"))

for (ii in 1:nrow(mat)) {
  #if(length (which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])) != 0 ) rownames(mat) [ii] = geneannotation1$external_gene_name [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
  rownames(mat) [ii] = unique(geneannotation1$external_gene_name) [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
}

mycol= c("red","white","yellow")
mylabel = select_hist[,c("sample.nameNEW","final.molec","IDH2","RHOA","TET2","DNMT3A")]
rownames(mylabel) = mylabel$sample.nameNEW
mylabel$sample.nameNEW = NULL
mylabel.nocol = mylabel
mylabel.col = mylabel
mylabel.col[is.na(mylabel.col)]<-0
#head(mylabel.col)
mylabel.col$final.molec[mylabel.col$final.molec == "AITL"] = "black"; mylabel.col$final.molec[mylabel.col$final.molec == "PTCL.nos"] = "orange"
for (a in 2:5) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol )

mat  <- mat - rowMeans(mat)
par(mfrow=c(1,1))
cluster.pts.nr = pheatmap(mat, annotation_col = mylabel.nocol, annotation_colors = list(final.molec = c(AITL = "black", PTCL.nos = "orange"), filename= "x.pdf",
                                  IDH2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  RHOA = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  TET2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  DNMT3A = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]) ) , show_colnames = F, cellheight = 15, 
         border_color= NA, color = colorRampPalette(rev(brewer.pal(n = 5 , name = "RdYlBu")))(20), scale = "row", clustering_method = "ward.D2",clustering_distance_cols = "euclidean" , silent = F)

### export pts order
cluster.pts.nr$tree_col$labels [cluster.pts.nr$tree_col$order]
cluster.pts.nr$tree_col$labels 
#pheatmap::pheatmap(test, filename="test.pdf")
```


LOOCV on AILT, PTCLnos based on 16-gene model

```{r}
y = t(mat)
cl.orig = c()
for (u in 1:nrow(y)) cl.orig [u] = unlist(strsplit(rownames(y)[u],"\\."))[1]

perm.mother = rownames(y)
perm.son = combn (perm.mother, length(perm.mother)-1)

output <- cbind(perm.mother, NA)

for (i in 1:length(perm.mother)) {
  train <- y [ perm.son[,i], ]
  test <- y [ ! ( rownames(y) %in% perm.son[,i]) , ]
  cl <- cl.orig [which(rownames(y)%in%perm.son[,i])]
  z <- lda(train, cl)
  p <- predict(z,test)$class
  output  [ setdiff(1:271, which( rownames(y) %in% perm.son[,i]) ) , 2  ] = as.character(p)
#  output  [ output[,1] == rownames(test) , 3  ] = z$scaling [1,1]
#  output  [ output[,1] == rownames(test) , 4  ] = z$scaling [2,1]
#  output  [ output[,1] == rownames(test) , 5  ] = z$scaling [3,1]
}

colnames(output) = c("true","LOOCV.predicted")
output = as.data.frame(output)
output$true.class = cl.orig

table(output$true.class, output$LOOCV.predicted  )
confusionMatrix(table(output$true.class, output$LOOCV.predicted  ))

# Confusion Matrix and Statistics
# 
#       
#        AITL PTCL
#   AITL  106   21
#   PTCL   16  128
#                                          
#                Accuracy : 0.8635         
#                  95% CI : (0.8168, 0.902)
#     No Information Rate : 0.5498         
#     P-Value [Acc > NIR] : <2e-16         
#                                          
#                   Kappa : 0.7252         
#  Mcnemar's Test P-Value : 0.5108         
#                                          
#             Specificity : 0.8591         
#          Pos Pred Value : 0.8346         
#          Neg Pred Value : 0.8889         
#              Prevalence : 0.4502         
#          Detection Rate : 0.3911         
#    Detection Prevalence : 0.4686         
#       Balanced Accuracy : 0.8640         
#                                          
#        'Positive' Class : AITL      

```

Use ConsensusClusterPlus to extract most significant clusters: analyze sample stratification based on the extracted differentially expressed genes betwee AILT and PTCL-nos and the ALCL ALK-negative 3-gene model.

```{r}
select_hist<- pts.info.data[pts.info.data$final.molec == "AITL" | pts.info.data$final.molec == "PTCL.nos" | pts.info.data$final.molec == "ALCL.neg",]
# Add three classifier genes for ALCL ALK-neg [Agnelli et al, Blood, 2012]
# Check on array
anaplastic_gene<- c("TNFRSF8","BATF3","TMOD1")
geneannotation2 <- getBM( attributes = c("entrezgene", "external_gene_name"), filters = "external_gene_name", values = anaplastic_gene, mart = ensembl )

anaplastic_gene_ARRAY<- paste0(geneannotation2$entrezgene, "_at")

# Append 16-gene model to 3-gene model
list_genes_all<- c(list_genes, anaplastic_gene_ARRAY)

# Redo consensus cluster analysis
gep<- geneExpr[,select_hist$sample.nameNEW]
mat<- gep[list_genes_all,]
title=tempdir()
d<- data.matrix(mat)
d = sweep(d,1, apply(d,1,median,na.rm=T))
results = ConsensusClusterPlus(d,maxK=8,
                               pFeature=1,
                               title=title,
                               clusterAlg="hc",
                               innerLinkage="ward.D2",
                               finalLinkage="ward.D2",
                               distance="euclidean",
                               seed=123456789)
kk<- as.data.frame((results[[5]]$consensusClass)) ##### 4 significant cluster
kk$geo.id<- rownames(kk)
colnames(kk)[1]<- "cluster"
table(kk$cluster)
```

Plot heatmap  AITL, PTCL-NOS, ALCL-neg and the 19-gene model

```{r fig.width = 15, fig.height = 10}

heat<- merge(t(mat), kk, by.x = 0, by.y="geo.id")
heat2<- merge(heat, pts.info.data, by.x = 1, by.y="sample.nameNEW")
heat2<- heat2[order(heat2$cluster),]
mycol= c("red","white","yellow")
mylabel = heat2[,c("Row.names","cluster","final.molec","TET2","RHOA","IDH2","DNMT3A")]
colnames(mylabel)<- c("sample.names","clusters","Histology","TET2","RHOA","IDH2","DNMT3A")
rownames(mylabel) = mylabel$sample.names
mylabel$sample.names  = NULL
mylabel.nocol = mylabel
mylabel.col = mylabel
mylabel.col[is.na(mylabel.col)]<-0
#head(mylabel.col)
mylabel.col$Histology[mylabel.col$Histology == "AITL"] = "black"; mylabel.col$Histology[mylabel.col$Histology == "PTCL.nos"] = "orange"; mylabel.col$Histology[mylabel.col$Histology == "ALCL.neg"] = "yellow"
for (a in c(3:6)) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol )
mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Set2"))
for (a in 1) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol_plus[1:5] )
mylabel.nocol$clusters<-as.numeric(as.character(mylabel.nocol$clusters))
mylabel.nocol$clusters<-as.character(paste("cluster",mylabel.nocol$clusters, sep=""))
  
par(mfrow=c(1,1))
par(mar=c(5,5,5,5), xpd=F)
mat3<- t(data.matrix(heat2[,2:20]))
colnames(mat3)<-heat2$Row.names
mat3= mat3[order(rownames(mat3)),]
temp_name = getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",rownames(mat3)), mart = ensembl)[,c(2:3)]
temp_name = temp_name[!duplicated(temp_name[,1]),]
rownames(mat3) = temp_name$external_gene_name
mat3  <- mat3 - rowMeans(mat3)
par(mfrow=c(1,1))
#pheatmap(mat3, annotation_col = mylabel.nocol, annotation_colors = list(clusters = c(cluster1= mycol_plus[1], cluster2 = mycol_plus[2], cluster3 = mycol_plus[3], cluster4 = mycol_plus[4], cluster5 = mycol_plus[5]), Histology = c(AITL = "black", PTCL.nos = "orange", ALCL.neg= "yellow"), IDH2 = c(MUT=mycol[1], "NA"=mycol[2],WT=mycol[3]), RHOA = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]), TET2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]), DNMT3A = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]) ) ,  border_color= NA, color = colorRampPalette(rev(brewer.pal(n = 5 , name = "RdYlBu")))(100), scale = "row", cluster_cols = FALSE, show_colnames= F, row_annotation =3, cellheight = 20)
#dev.off()
# print with gaps
num_clust<- as.numeric(table(mylabel.nocol$clusters))
num<- c(num_clust[1], sum(num_clust[1:2]),sum(num_clust[1:3]),sum(num_clust[1:4]),sum(num_clust[1:5]) )
par(mfrow=c(1,1))
pheatmap(mat3, annotation_col = mylabel.nocol, annotation_colors = list(clusters = c(cluster1= mycol_plus[1], cluster2 = mycol_plus[2], cluster3 = mycol_plus[3], cluster4 = mycol_plus[4], cluster5 = mycol_plus[5]), Histology = c(AITL = "black", PTCL.nos = "orange", ALCL.neg= "yellow"), IDH2 = c(MUT=mycol[1], "NA"=mycol[2],WT=mycol[3]), RHOA = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]), TET2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]), DNMT3A = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]) ) ,  border_color= NA, color = colorRampPalette(rev(brewer.pal(n = 5 , name = "RdYlBu")))(100), scale = "row", cluster_cols = FALSE, show_colnames= F, row_annotation =3, cellheight = 20,  gaps_col = num)
# gaps_col=c(0,rep(0,num[1]-1), 40,rep(0,num[2]-1), 1000,rep(0,num[3]-1), 40,rep(0,num[4]-1), 40,rep(0,num[5]-1)))
        

gep<- geneExpr[,select_hist$sample.nameNEW]
mat<- gep[list_genes_all,]
geneannotation1 <- getBM( attributes = c("ensembl_transcript_id", "entrezgene", "external_gene_name"), filters = "entrezgene", values = gsub("_at","",list_genes_all), mart = ensembl)
sort(unique(geneannotation1$external_gene_name))
setdiff(rownames(mat), paste0(unique(geneannotation1$entrezgene),"_at"))

for (ii in 1:nrow(mat)) {
  #if(length (which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])) != 0 ) rownames(mat) [ii] = geneannotation1$external_gene_name [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
  rownames(mat) [ii] = unique(geneannotation1$external_gene_name) [ which (paste0(unique(geneannotation1$entrezgene),"_at") == rownames(mat)[ii])]
}

mycol= c("red","white","yellow")
mylabel = select_hist[,c("sample.nameNEW","final.molec","IDH2","RHOA","TET2","DNMT3A")]
rownames(mylabel) = mylabel$sample.nameNEW
mylabel$sample.nameNEW = NULL
mylabel.nocol = mylabel
mylabel.col = mylabel
mylabel.col[is.na(mylabel.col)]<-0
#head(mylabel.col)
mylabel.col$final.molec[mylabel.col$final.molec == "AITL"] = "black"; mylabel.col$final.molec[mylabel.col$final.molec == "PTCL.nos"] = "orange"; mylabel.col$final.molec[mylabel.col$final.molec == "ALCL.neg"] = "yellow"
for (a in 2:5) mylabel.col[,a] = factor(mylabel.col[,a], levels = levels(as.factor(mylabel.col[,a])), labels = mycol )

mat  <- mat - rowMeans(mat)
par(mfrow=c(1,1))
pheatmap(mat, annotation_col = mylabel.nocol, annotation_colors = list(final.molec = c(AITL = "black", PTCL.nos = "orange", ALCL.neg = "yellow"), filename= "x.pdf",
                                  IDH2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  RHOA = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  TET2 = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]),
                                  DNMT3A = c(MUT=mycol[1],"NA"=mycol[2],WT=mycol[3]) ) , show_colnames = F, cellheight = 15, 
         border_color= NA, color = colorRampPalette(rev(brewer.pal(n = 5 , name = "RdYlBu")))(20), scale = "row", clustering_method = "ward.D2",clustering_distance_cols = "euclidean" , silent = F)

### export pts order
cluster.pts.nr$tree_col$labels [cluster.pts.nr$tree_col$order]

```

## Cibersort algorithm to characterize the tumour and microenviroment composition of each cluster

```{r fig.width = 20, fig.height = 7, warning=F}

##### cibersort and origical molecular histologies
load("./Rmd.files/cibersort.all.Rdata")
ciber_all<-as.data.frame.matrix(t(cibersort.percentages))
ciber_all$sample.nameNEW <- rownames(ciber_all)
colnames(kk)[2]<-"sample.nameNEW"
require(plyr)
final <-join(ciber_all, kk, by = "sample.nameNEW",  type="left")
final2<-merge(pts.info.data[,c(1,6,14:17)], final, by="sample.nameNEW")
final3<- subset(final2, final.molec %in% c("AITL","ALCL.neg","ALCL.pos","ATLL","NKT","PTCL.nos"))
final3<- final3[order(final3$final.molec),]
library(RColorBrewer)
n <- 22
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))

par(mar=c(2,5,7,10), xpd=TRUE)
x<- barplot(t(final3[7:28]), names.arg = rep("", length(final3$final.molec)), cex.names = 0.7, col=col_vector, border=NA,
            space=rep(0, nrow(final3)))
legend("topright",legend=colnames(final3)[7:28], col=col_vector, pch=c(15), inset=c(-0.11,0), pt.cex= 1,
cex = 1, bty = "n",  x.intersp = 0.7)


names_hist<- unique(final3$final.molec)
col_hist<- c("orange","yellow","dodgerblue2","brown2","darkorchid1","black")
num<- as.numeric(table(final3$final.molec))
for(i in (1:length(num)))
{
  segments(x[sum(num[1:i])+1-num[i]], 1.05,x[sum(num[1:i])],1.05,lwd=4, col=col_hist[i])
  text(x[(sum(num[1:i])-num[i] +1+ sum(num[1:i]))/2], 1.1, names_hist[i], cex=1.2, srt=0)

}

```


```{r fig.width = 20, fig.height = 7, warning=F}


##### plot cibersort profile of patients stratified according to histology and clusters

for(i in (1:nrow(final3)))
{
final3$cluster[i][is.na(final3$cluster[i])]<- final3$final.molec[i]
}

final3$cluster <- factor(final3$cluster, levels = c( "1","2","4","NKT","3","5","ALCL.pos", "ATLL"))

final3<- final3[order(final3$cluster),]

#pdf("barplot_cibersort.pdf", width = 20, height = 7)
par(mar=c(2,5,7,10), xpd=TRUE)
x<- barplot(t(final3[7:28]), names.arg = rep("", length(final3$final.molec)), cex.names = 0.7, col=col_vector, border=NA,
            space=rep(0, nrow(final3)))
legend("topright",legend=colnames(final3)[7:28], col=col_vector, pch=c(15), inset=c(-0.11,0), pt.cex= 1,
cex = 1, bty = "n",  x.intersp = 0.7)

mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Dark2"))
names_hist<- c("C-1","C-2", "C-4","NKT","C-3","C-5","ALCL.pos","ATLL")
col_hist<- c(mycol_plus[1],mycol_plus[2],mycol_plus[4],"darkorchid1",mycol_plus[3],mycol_plus[5],"dodgerblue2","brown2","")
num<- as.numeric(table(final3$cluster))
  par(new=TRUE)
for(i in (1:(length(num))))
{
  segments(x[sum(num[1:i])+1-num[i]], 1.05,x[sum(num[1:i])],1.05,lwd=4, col=col_hist[i])
  text(x[(sum(num[1:i])-num[i] +1+ sum(num[1:i]))/2], 1.1, names_hist[i], cex=1.2, srt=0)

}
# dev.off()

```

Boxplot comparing the contribution of each cibersort signature between all extracted clusters

```{r fig.width = 10, fig.height = 5, warning=F}

par(mfrow=c(1,2))
par(mar=c(3,3,3,3), xpd=F)
for(i in (7:27))
{
  #pdf(sprintf("%s_cibersort_ptcl.pdf",i), height=8, width=10)
  k<- as.numeric(final2[,i])
  table_wilk<- pairwise.wilcox.test(k,final2$cluster,p.adjust.methods = "bonferroni" )$p.value
  df_wilk <- data.frame(expand.grid(dimnames(table_wilk)),array(table_wilk))
  df_wilk2<-na.omit(df_wilk)
  df_wilk2_sig<- df_wilk2[df_wilk2$array.table_wilk.<0.05,]
  df_wilk2_sig$Var1<-as.numeric(as.character(df_wilk2_sig$Var1))
  df_wilk2_sig$Var2<-as.numeric(as.character(df_wilk2_sig$Var2))
  if(nrow(df_wilk2_sig)>0)
  {
  boxplot(k~final2$cluster, ylim=c(0,(max(k)+0.2)), main=colnames(final2)[i], cex.main=2, col=mycol_plus, las=2)
  for(j in (1:nrow(df_wilk2_sig)))
  {
    segments(df_wilk2_sig$Var1[j], max(k)-0.01+j/30, df_wilk2_sig$Var2[j],max(k)-0.01+j/30)
    p<-df_wilk2_sig$array.table_wilk.[j]
    if(p<0.00001){p2 = "<0.00001"}else{
    p2<-as.numeric(formatC(p,digits=6,format="f"))}
    pval <- paste("p =",p2,sep=" ")
    text((df_wilk2_sig$Var1[j]+ df_wilk2_sig$Var2[j])/1.9,  max(k) +j/30, pval, cex=0.8)
  }
    }
  #dev.off()   
   }

```

## R tmod package analysis 

```{r fig.width = 10, fig.height = 5, warning=F}

# for convenience: reimport annotated matrix
final<- read.delim("./Rmd.files/aitl_nos_alcl_clsutering.txt",sep="\t",header = T,stringsAsFactors = F)
final2<- final[,c("Row.names","hist","cluster")]
mat<- read.delim("./Rmd.files/ensembl_annotated_matrix.txt", sep="\t", stringsAsFactors = F)

design <- model.matrix(~ 0+factor(final2$cluster)) ##### create matrix
colnames(design)<-paste0("Cluster_",c(1:5))
contrast.matrix <- makeContrasts(Cluster_2-Cluster_1, Cluster_3-Cluster_1,Cluster_4-Cluster_1, Cluster_5-Cluster_1,
                                 Cluster_3-Cluster_2, Cluster_4-Cluster_2, Cluster_5-Cluster_2,
                                 Cluster_4-Cluster_3, Cluster_5-Cluster_3,
                                 Cluster_4-Cluster_5,
                                 Cluster_2-(Cluster_1 + Cluster_3 + Cluster_4 + Cluster_5)/4,
                                 Cluster_3-(Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4,
                                 Cluster_4-(Cluster_1 + Cluster_2 + Cluster_3 + Cluster_5)/4,
                                 Cluster_1-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4,
                                 Cluster_5-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4,
                                 levels=design)
fit1 <- lmFit(mat, design)
fit2 <- contrasts.fit(fit1, contrast.matrix)
fit <- eBayes(fit2)

geneExpr = adj.data
geneExpr2<- geneExpr[,colnames(geneExpr) %in% final2$Row.names ]
geneExpr2<- geneExpr2[,final2$Row.names]
ensembl = useMart( "ensembl", dataset = "hsapiens_gene_ensembl" )
hgnc <- getBM(attributes=c('entrezgene','hgnc_symbol','hgnc_id'),filters = 'entrezgene', values = gsub("_at","",rownames(geneExpr2)),mart = ensembl)
geneExpr3<- as.data.frame.matrix(geneExpr2[which(rownames(geneExpr2) %in% paste0(hgnc$entrezgene,"_at")),])

levels_design<- c("Cluster_2-Cluster_1","Cluster_3-Cluster_1","Cluster_4-Cluster_1","Cluster_5-Cluster_1",
                 "Cluster_3-Cluster_2","Cluster_4-Cluster_2","Cluster_5-Cluster_2","Cluster_4-Cluster_3",
                 "Cluster_5-Cluster_3","Cluster_4-Cluster_5",
                 "Cluster_2-(Cluster_1 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_3-(Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4",
                 "Cluster_4-(Cluster_1 + Cluster_2 + Cluster_3 + Cluster_5)/4",
                 "Cluster_1-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_5-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4")
df_diff_all=NULL
for(i in (1:length(levels_design)))
{
tt <- topTable(fit, coef=i, number=Inf, genelist=rownames(geneExpr3))
tt$ID<- rownames(tt)
colnames(tt)[1]<-"GENE_SYMBOL"
head(tt, 10)
fg <- tt$GENE_SYMBOL[tt$adj.P.Val < 0.001 & abs( tt$logFC ) > 2]
length(fg)
df_diff<- cbind(fg, rep(levels_design[i], length(fg)))
df_diff_all<-rbind(df_diff_all, df_diff)
#plot(tt$logFC, -log10(tt$adj.P.Val))
}
df_diff_all<- as.data.frame.matrix(df_diff_all)

annotation_col<- final2
colnames(annotation_col)<-c("sampleID","Hist","cluster")
A <- function(x) (as.factor(as.character(x))) ##### lapply function for all columns to generate the relative contribution
annotation_col[,1:ncol(annotation_col)] = apply(annotation_col[,1:ncol(annotation_col)], 2, function(x) as.factor(as.character(x)))
annotation_col<- as.data.frame(annotation_col[,-1])
mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Dark2"))
ann_colors = list(Hist=c( "AITL"="black","ALCL"="yellow","PTCL"="orange"),
                  cluster=c("1" = mycol_plus[1],"2" = mycol_plus[2],"3" = mycol_plus[3],"4" = mycol_plus[4],"5" =mycol_plus[5])
                  )

edata3<- mat[rownames(mat) %in% unique(df_diff_all$fg),]
pheatmap(as.matrix( as.matrix(edata3)), annotation_col=annotation_col, annotation_colors = ann_colors, border_color="NA", scale = "row", cluster_cols = FALSE, show_colnames= F, show_rownames = FALSE)

levels_design<- c("Cluster_2-Cluster_1","Cluster_3-Cluster_1","Cluster_4-Cluster_1","Cluster_5-Cluster_1",
                 "Cluster_3-Cluster_2","Cluster_4-Cluster_2","Cluster_5-Cluster_2","Cluster_4-Cluster_3",
                 "Cluster_5-Cluster_3","Cluster_4-Cluster_5",
                 "Cluster_2-(Cluster_1 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_3-(Cluster_1 + Cluster_2 + Cluster_4 + Cluster_5)/4",
                 "Cluster_4-(Cluster_1 + Cluster_2 + Cluster_3 + Cluster_5)/4",
                 "Cluster_1-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_5)/4",
                 "Cluster_5-(Cluster_2 + Cluster_3 + Cluster_4 + Cluster_1)/4")
df_diff_all=NULL
for(i in (1:length(levels_design)))
{
tt <- topTable(fit, coef=i, number=Inf, genelist=rownames(geneExpr3))
tt$ID<- rownames(tt)
colnames(tt)[1]<-"GENE_SYMBOL"
head(tt, 10)
fg <- tt$GENE_SYMBOL[tt$adj.P.Val < 0.001 & abs( tt$logFC ) > 2]
length(fg)
df_diff<- cbind(fg, rep(levels_design[i], length(fg)))
df_diff_all<-rbind(df_diff_all, df_diff)
#plot(tt$logFC, -log10(tt$adj.P.Val))
}
df_diff_all<- as.data.frame.matrix(df_diff_all)
table(df_diff_all$V2)
annotation_col<- final2
colnames(annotation_col)<-c("sampleID","Hist","cluster")
A <- function(x) (as.factor(as.character(x))) ##### lapply function for all columns to generate the relative contribution
annotation_col[,1:ncol(annotation_col)] = apply(annotation_col[,1:ncol(annotation_col)], 2, function(x) as.factor(as.character(x)))
annotation_col<- as.data.frame(annotation_col[,-1])
mycol_plus<- c(brewer.pal(11,"Paired"),brewer.pal(6,"Dark2"))
ann_colors = list(Hist=c( "AITL"="black","ALCL"="yellow","PTCL"="orange"),
                  cluster=c("1" = mycol_plus[1],"2" = mycol_plus[2],"3" = mycol_plus[3],"4" = mycol_plus[4],"5" =mycol_plus[5])
                  )
edata3<- mat[rownames(mat) %in% unique(df_diff_all$fg),]
pheatmap(as.matrix( as.matrix(edata3)), annotation_col=annotation_col, annotation_colors = ann_colors, border_color="NA",
         scale = "row", cluster_cols = FALSE, show_colnames= F, show_rownames = FALSE)
############ table of genes
df_diff_all_tab=NULL
for(i in (1:length(levels_design)))
{
  tt <- topTable(fit, coef=i, number=Inf, genelist=rownames(geneExpr3))
  tt$ID<- rownames(tt)
  colnames(tt)[1]<-"GENE_SYMBOL"
  head(tt,10)
  fg <- tt[tt$adj.P.Val < 0.001 & abs( tt$logFC ) > 2,]
  if(nrow(fg)>0){
    fg$design<- levels_design[i]
  df_diff_all_tab<-rbind.data.frame(df_diff_all_tab, fg)
  #plot(tt$logFC"," -log10(tt$adj.P.Val))
  }
  }
nrow(df_diff_all_tab) #### number of genes differentially expressed between C-1, C-2, C-3, C-4, C-5
##### list gene from Iqbal et al. blood 2014
iqbal<- unique(c("EFNB2","ROBO1","S1PR3","ANK2","LPAR1","SNAP91","SOX8","LPAR1","RAMP3","S1PR3","ROBO1","EFNB2","TUBB2B","SOX8",
                 "SOX8","ARHGEF10","DMRT1",  "SLC19A21","STK3","PERP","TNFRSF8","TMOD1","BATF3","CDC14B","PERP","WDFEY3",
                 "TMOD1","ATP6V0D1","AXL","CD59","CHI3L1","CLTC","COL6A1","CREG1","CTSB","CTSC","NR1","H3","PDXK","PITPNA",
                 "PLSCR1","PRDX3","CTSS","CYBB","FABP3","FPR1","FTL","GUCA2A","HCK","IFI30","IL13RA1","JAK2","LILRB1",
                 "PRKG1PSAP","SLC7A7","SOD2","TCN2","THY1","TYR","UBE2L6","WARS","AXL","FTL","SIRPA","STAT1","CSF2","IFNG",
                 "SEPT6","GATA3","CD28","STAT1","AXL","CD28","CD40","CD59","CSF2","FTL","IFNG","LILRB1","SIRPA","TBX21",
                 "MSH6","EGR1","CAT","EGR1","CAT"))
intersect(iqbal, unique(df_diff_all_tab$GENE_SYMBOL))

```
# pairwise for pathway using tmod (https://cran.r-project.org/web/packages/tmod/vignettes/tmod.pdf)

```{r fig.width = 20, fig.height = 20, warning=F }
fit1 <- lmFit(mat, design)
fit2 <- contrasts.fit(fit1, contrast.matrix)
fit <- eBayes(fit2)
res.l <- tmodLimmaTest(fit, rownames(mat))
length(res.l)
names(res.l)
pie <- tmodLimmaDecideTests(fit, genes=rownames(mat))
par(mfrow=c(1,1))
res.l2<- lapply(res.l, function(x) {x[x$adj.P.Val<10e-8,]})
tmodPanelPlot(res.l2, pie=pie, text.cex=0.6) ##### zero = grey, blue down in the first factor and red up in the first
res.l2<- lapply(res.l, function(x) {x[x$adj.P.Val>10e-8 & x$adj.P.Val<10e-5,]})
tmodPanelPlot(res.l2, pie=pie, text.cex=0.6) ##### zero = grey, blue down in the first factor and red up in the first
```
